Early Stage Researchers

Applications are invited for 15 Early-Stage Researcher (ESR) positions to be funded by the Marie-Sklodowska-Curie European Training Network on “PErsonalized Robotics as SErvice Oriented applications” (PERSEO) within the Horizon 2020 Programme of the European Commission.

The network will train 15 early-stage researchers in the context of research and development for “PErsonalized Robotics as SErvice Oriented applications”. The consortium is formed by 8 prestigious European Universities and 2 leading robotics businesses plus 9 partner organisations to cover a full training programme on scientific, technical, and personal development skills. Placements will include secondments in current world-leading organisations in Spain, Germany, Austria, UK, Italy, Denmark, the Netherlands, etc. to gather the necessary knowledge and implement it in Europe. Thus, collaboration within the network is strongly encouraged.

Candidates 

We are looking for talented and highly motivated early career researchers with a degree giving access to PhD studies in a domain relevant to the ESR position(s) of interest (such as Computer Science, Psychology, Robotics, Law, Engineering, Philosophy and Ethics etc). We expect dedication and enthusiasm for experimental research, combined with scientific curiosity and the capacity to teamwork in an interdisciplinary environment. Preferred qualifications include excellent grades, research talent and personal ambition. Applicants should have excellent command of English, together with good academic writing and presentation skills. 

Fulltime employments as PhD students are offered for 3 years. The starting date of the contract is negotiable, but ideally it would be August 2021.

It is strongly recommended to contact potential supervisors via E-mail before submitting an application.

Benefits

PERSEO fellows will benefit from:

  • Outstanding training aimed at fostering scientific, technical and transferable skills
  • Short-term secondments in the PERSEO network
  • Scientific advice from internationally recognized experts
  • A solid mentoring strategy followed up through a personalised career development plan.

The position will permit to carry out research in a stimulating group with the chance to join high-level training events. You will get a very attractive salary plus an allowances package in accordance with the Marie Skłodowska-Curie Actions (MSCA) rules and the personal circumstances of the applicant. The ESR salary is subject to local tax, social benefit and other deductions following national regulations. The exact salary will be confirmed upon appointment and is dependent on the country correction factor (to allow for the difference in cost of living in different EU Member States). The PhD students will be employed with full social security coverage and all benefits in accordance with the Marie Sklodowska-Curie ITN fellowship regulations of the European Union.

For further information please visit the MSCA website.

Eligibility criteria

European and non-European students are invited to apply to any of the 15 fellowships. The applicant can select different positions providing a preference ranking.

To be eligible, the candidate:

  • must be in the first four years of her/his research career. Candidates should ideally possess a master’s degree in a relevant academic field or a comparable degree that allows them to embark in a PhD (applications from candidates who already possess a doctoral degree cannot be considered).
  • must not have resided or carried out their main activity (work, studies, etc.) in the country of the institution that recruits the student for more than 12 months in the 3 years immediately before the recruitment date. Short stays such as holidays and/or compulsory national service are not taken into account.
  • must be willing to spend a prolonged time in another institute/country of the network during the PhD thesis period.

For further details on eligibility criteria please check in the Information Note for ITN Fellows.

Selection process

SELECTION OF CANDIDATES

Selection of candidates will be performed via online interview. In case the number of candidates for a position exceeds the number of open positions, shortlisted candidates will selected on the basis of the fit of their CV and research interests with the chosen research project(s). These candidates will be invited for interview and positions will be offered after approval by the PERSEO Committee.

Applicants who apply for more than one individual research project should indicate the order of preference (e.g. 1st, 2nd and 3rd choice).

To ensure equality of opportunities, we strongly encourage women with the appropriate qualifications to apply. If equally qualified, participants with a handicap will be preferred.

DOCUMENTS TO BE SENT

1. Application form [download]

2. a copy of the passport

3. a full CV

4. a 2-page motivation letter including a description of previous research experience (1 page) and current research interest, outlining the fit to the desired ESR (1 page)

5. one or more reference letters and names of 2 referees including contact details

6. Transcripts of grades obtained for B.Sc. and M.Sc. degrees, expected final grade

7. Certificates  of academic degrees

8. List of Publications (if any)

Only documents in English language will be accepted.

Applicants should submit the documentation exclusively via e-mail in ONE PDF document to applications@perseo.eu (max file size 5 MB) by 1 April, 2021 at 23 pm (GMT). Your application will be jointly processed by the members of the PERSEO consortium and treated according to the GDPR.

FURTHER INFORMATION

Note that some projects may have specific requirements concerning the documentation to submit and the application procedure for the specific project. The candidate must verify that the application includes all required documents and must follow the exact procedure for the ESR of interest.

Details about the 15 PhD positions and the specific requirements for applicants are available below. Please make sure to consult the ESR-specific requirements.

Moreover, it is strongly recommended to contact potential supervisors via E-mail before submitting an application.

PROJECTS DESCRIPTION

Personalization in physical interaction

BENEFICIARY: ORTELIO L.T.D.

OBJECTIVES: In this project, the ESR will develop a cloud-based, GPU compatible SLAM library to enable autonomous robotic movement via the cloud. Both GPU and CPU processing will be supported. The structure of the library will be compatible with the NOOS robotic app store – RAPP – which means that containers (such as Kubernetes) will be used in conjunction with Python, ROS and KERAS. The library will support a wide number of actuators and sensors by focusing on hardware abstraction via modelling instead of explicit support on multiple specific sensors. From the end-user point of view, the library will enable robots with increased autonomy and context awareness, especially with respect to contact with humans. Robots will be able to reconfigure their tasks as and when the task context changes. Possible obstacles, machinery or humans can be detected and recognised as such by the use of cloud data processing. Real-time maps can be generated, and new paths and motion planning can be drawn.

EXPECTED RESULTS: An open-source library, to be used in the cloud, that can manage the Localization and Mapping of a robot in dynamic environments like hospitals, elderly care homes, or elderly people’s houses. The aim the library is to that allow an easy personalized configuration of the robot in a new environment when it is required to accomplish tasks that include goal-oriented and user-oriented navigation.

BENEFICIARY: UNIMORE

OBJECTIVES: The aim of the project is to research new algorithms for people recognition, that effectively scale to real-world data. This project will research and design novel algorithms for human body pose estimation during interactions by effectively mix convolutional networks for body part location with regression network for 3D pose estimation in conjunction with differentiable rendering techniques capable of projecting parametric body model on the scene and optimizing the pose estimation process through plain back-propagation. The objective will be to provide real time pose estimation algorithms and exploiting the extracted 2D and 3D body poses for classifying and eventually predicting actions. A possible research line will be the exploitation for the latter task of generative recurrent models trained for both action classification and intention prediction. This task has been effectively applied to pedestrian trajectories, but a very limited number of pioneering works deal with complex full-body human actions and are limited to surveillance scenarios.

EXPECTED RESULTS: State of the art algorithms for real time body pose estimation that can be used by the robot for planning and deciding the proper strategy for the HRI. Generative models for action classification and intention prediction in order to potentially generate multiple futures and provide the robots several hypotheses on which the planning and control can be based. In synergy with concurrent European projects (MOVECARE, WISER), we will test the recognition of typical home objects (glasses, remote controllers, keys, etc.) which exhibit different point of manipulation and interaction modalities.

BENEFICIARY: TUM

OBJECTIVES: ESR3 will exploit how to transfer complex tasks to the robot by using kinaesthetic demonstrations, without the need for expert knowledge or advanced programming skills of the user. Such tasks include multi-modal conditions such as positions, forces or grasp status. Both internal joint torque sensors and external force torque sensor will be leveraged, in order to learn and generate robot behaviours, which depend on the contact location with the human or the environment. This shall be achieved by an interactive teaching scheme between user and robot. Hereby, the robot shall request further demonstrations of the user in order to resolve unseen situations and to add alternative behaviours according to the environmental state.

EXPECTED RESULTS: A robot will learn how to adapt to environmental conditions. This will be learned from expert’s demonstration in order to be used in rehabilitation settings. Desired interaction force profile will be identified and learned while a human teacher demonstrates the interaction tasks with envi- ronment via kinaesthetic teaching. The desired interaction force for the tasks and the interaction force from human’s physical guidance will be distinguished.

Requirements:

–  Master degree or equivalent in Robotics, Computer science, and Engineering
–  Excellent mathematical and coding skills (C/C++, Matlab, ROS, Python).
–  Excellent skills and experience in machine learning, robotics, and control

Other elements (not requirements):

– Master Thesis

BENEFICIARY: PAL Robotics

OBJECTIVES: User-centred design and development of novel imitation learning techniques for enhancing the TIAGo robot. The goal is to learn human actions while modelling the physical capabilities of the human and mapping them on the robot. This includes upgrading the robot’s hardware and software (100% ROS compatible) according to the experimental results.

EXPECTED RESULTS: A new set of approaches to program the manipulator of a mobile robot via intuitive and natural methods for the human user. The outcome of the project will be a social robot able to learn activities from a human demonstrator using visual observation and imitating the actions taking the different physical capabilities of the human and the robot into account. Experiments and validation with potential users.
 
Requirements:
– degree giving access to PhD in Robotics, Computer Science, or related ones
– advanced C++ programming skills
– intermediate ROS knowledge
– intermediate Robotics knowledge

BENEFICIARY: UNINA

OBJECTIVES: The project aims to investigate the role of dynamical properties of the body movements as well as non-verbal cues in making the robot motion and interaction behaviour legible as much as possible, in order to allow the user predicting and estimating the interaction state or the robot intention. Both a by-design approach as the possibility of learning such behaviour will be considered. To take into account user’s preferences, interactive machine learning will be explored as a meaningful learning method where leaned behaviour can take into account the individual user’s preferences. This introduces new challenges that require a better understanding of the functionality and needs of the end user, but also a number of modelling challenges on how interactively include the target person in the learning “cycle” considering both direct and indirect feedback and/or personal goals an integral part of the model.

EXPECTED RESULTS: The proposed behavioural and learning models will be tested in a domain with both interaction tasks and non-interacting ones. Results will be evaluated in terms of the ability of the users in recognizing the intents beyond the robot movements and actions in the space and in predicting the next actions.

Requirements:
–  Master degree or equivalent in Robotics, Computer science, and Engineering
– Advanced programming skills

Other elements (not requirements):
– Experience of robot programming and/or human-robot interaction research

Personalization in COGNITIVE interaction

BENEFICIARY: TUM

OBJECTIVES: A novel human intention recognition model that integrates information on low‐level motions and higher‐level activity in order to achieve context-based human activity interpretation. Recognized activity, activity model, and context understanding are exploited to estimate future human activities or intentions. Human intention is predicted at two levels including the low-level motion trajectory and the high-level semantic activity with accompanying prediction confidence. Both levels are exploited to on-line adapt the robot’s behaviour for the successful execution of physical HRI tasks.

EXPECTED RESULTS: Effective solutions for fast and accurate human activity recognition and prediction will be developed and integrated into a framework of learning from demonstrations. It will allow on-line robot’s behaviour seamless adaptation during the execution of collaborative tasks.

Requirements:

–  Master degree or equivalent in Robotics, Computer science, and Engineering
–  Excellent mathematical and coding skills (C/C++, Matlab, ROS, Python).
–  Excellent skills and experience in machine learning, robotics, and control

Other elements (not requirements):

– Master Thesis

BENEFICIARY: UNIMORE

OBJECTIVES: To develop new approaches that bridge together perception, language and action in robotic scenarios, fostering a natural interaction between humans and robots. Objectives of the ESR include: (i) the development of language and vision-based navigation algorithms, in which a mobile agent is trained to perform actions or reach a target destination via natural language instructions; (ii) the investigation of solutions for interacting with robotic agents in natural language, by endowing the robot with the capability of describing its current state, and understanding inputs in natural language; (iii) the training of navigation and interaction algorithms on simulated environments and their deployment on real robots.

EXPECTED RESULTS: State-of-the-art algorithms for navigation and visual-semantic tasks which can bring the interaction between human and robots feasible in natural language, and which can effectively connect vision and language on robotic systems. Novel and state of the art approaches for real-time HRI in natural language, with a specific focus on semantically challenging domains. Deployment of such algorithms on real robots.

BENEFICIARY: UMAN

OBJECTIVES: This project aims at the design of a computational architecture for ToM. This will extend the ToM model developed by Vinanzi et al., with the specific goal to support personalized interaction. The design of ToM skills will be based on developmental principles, which show the incremental acquisition of ToM from pure mechanical agency representations to actional agency and meta-representations, as in Leslie’s ToM-System1-2 theory.

EXPECTED RESULTS: The project will lead to the design and test of a computational implementation of developmental ToM architecture in cognitive robots. This will follow the methods of developmental robotics, e.g. with the testing of three incremental ToM levels: mechanical, actional and meta-representational agency. These will take into consideration the individual differences and needs of each user, to support the personalization of the robot’s interaction with the user. Experiments will validate this cognitive architecture in experiments on joint task (e.g. joint manipulation of objects in a game-like scenarios, with the robot adapting its strategy to the personalized preferences of the user).

Requirements:

– Advanced programming skills

Other elements (not requirements):

– Experience of robot programming and/or human-robot interaction research
– Knowledge of machine learning methods

BENEFICIARY: SHU

OBJECTIVES: This project aims at user cognitive modelling for improving robot collaborative behaviour and make the human-robot interfaces more intuitive for the individual user. To this end, it will create a general model of how the user think and make decisions, then, use the data collected during the HRI experiments to refine the model and customise the interaction to the specific person and task. Variables to be monitored are those that affect human cognition, such as fatigue, emotion, stress, and distraction. The final model will allow the cognitive architecture to have the capacity to infer inner user intents, which are not always consistent with behaviour, and call upon expert systems for advice when needed.

EXPECTED RESULTS: This project goal is to create a modular cognitive architecture of the robot able to assess the inner cognitive status of the user and use this to reduce the cognitive load of the user and make more effective the collaboration. The robot will have the capacity to infer user intent from the interaction, store information from experiences similarly to human memory, and increasingly personalise the interaction.

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Requirements:

– Advanced programming skills

Other elements (not requirements):

– Experience of robot programming and/or human-robot interaction research
– Knowledge of machine learning methods

BENEFICIARY: SHU

OBJECTIVES: Exploit closer collaboration among cognitive robotics and education psychology for personalized robotic teaching assistants. Carry-out experi- ments in the classroom to collect data to build and train the robot’s cognitive architecture while studying children’s personal reactions to the robot. To this end, the project will mix neuro-cognitive modelling, computer vision and HRI interface design to provide robots of a controllable autonomy that can be programmed and supervised by teachers and parents.

EXPECTED RESULTS: A novel class of robotic teaching assistants that could behave like peers, i.e. capable to mimic the behaviours of children when learning mathematics. These robots can autonomously lead educational activities in form of a game, during which they interact with speech and gestures to guide the learner through learning procedures and prompt the children to identify errors in the robot behaviours. Raw data from children’s experiment will constitute an open benchmark database for testing novel machine learning algorithm.

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Requirements:

– Advanced programming skills

Other elements (not requirements):

– Experience of robot programming and/or human-robot interaction research
– Knowledge of machine learning methods

Personalization in SOCIAL interaction

BENEFICIARY: UMAN

OBJECTIVES: For successful HRI, a robot must know when, and how, to interact with one, or more people, and adapt its behaviour to the individual’s prefer- ences and states (e.g. emotion). This project will take inspiration from existing deep models for action selection in social HRI scenarios, complementing it with other CNN approaches to emotion and face recognition. This will be used for personalized interaction in a robot companion for the elderly, e.g. in collaboration with the H2020 MoveCare project.

EXPECTED RESULTS: Extend the initial training dataset collected by Romeo et al. for deep learning training experiments on action selection in social inter- action. This will include a variety of interactions between elderly and robots in a social care context. The ESR will then integrate such a model with other modules on face identification and emotion recognition, to achieve a personalized and adaptive social interaction architectures. Pilot experiments with target groups of elderly will validate the integrated architecture, for further revision and extension. Some experiments will integrate ESR8 work.

Requirements:

– Advanced programming skills

Other elements (not requirements):

– Experience of robot programming and/or human-robot interaction research
– Knowledge of machine learning methods

BENEFICIARY: UNINA

OBJECTIVES: Design and develop a set of solutions for a multimodal user interface to maintain engagement through personalized social interaction and to enrich such interaction with social cues that can influence persuasion. To pursue this goal, the project will hybridize domain knowledge with on-line learning of the social strategies that are more effective for each specific user. The expected benefit is the reduction of the time needed for familiarisation with the robot and the support of the long-lasting use of it, minimizing the risk of rejection by frustration. Selection and social colouring of recommendations and instructions will be introduced by balancing the candidate output with the estimated receptiveness and engagement of the user.

EXPECTED RESULTS: The developed system is expected to dynamically recognize the user state (e.g., engagement) and to adapt its social cues accordingly. A/B testing of the Social Robot/User interaction in a controlled environment, and then on a realistic scenario for recommendation purposes. The goal is to assess the system usability, and the user satisfaction for received recommendations.

Requirements:

–  Master degree or equivalent in Robotics, Computer science, and Engineering

– Advanced programming skills

Other elements (not requirements):

– Experience of robot programming and/or human-robot interaction research

BENEFICIARY: UNIBI

OBJECTIVES: The project will be grounded in empirical experimental studies that integrate the expertise in social robotics and social psychology represented by the applicants. Studies on cognitive vs. affective trust and their differential behavioural correlates will be realized using various robot platforms (e.g., PAL Robotics platforms or those available at UNIBI as Pepper, NAO, iCub or Meka). The degree of adaptation and personalization (e.g., through user preference profiles, similarity, etc.) will be manipulated, while user factors (e.g., gender, negative attitudes towards robots, prior robot experience, trust propensity, trust in automation, demographics etc.) will be assessed to contribute further to an extended understanding of human-robot trust, both in short-term and in long-term interactions. Moreover, cognitive and affective trust will be manipulated to identify and study differential behaviours that might result from high vs. low levels of cognitive vs. affective trust elicited by a robot.

EXPECTED RESULTS: Validate key aspects that determine judgments of cognitive and affective trust in robots, focusing on user- and robot-related character- istics. Shed light on core psychological determinants on the part of the user, while simultaneously identifying core robot features that predict differential levels of cognitive and affective trust in HRI. Explore the effects of the different types of trust on behaviours that are reflective of and specific for cognitive vs. affective trust, respectively. As a use case, a human-robot learning/gaming paradigm will be validated. The results will provide guidelines on how to increase the development and maintenance of long-term trust in robots through personalization and adaptation.

Requirements:

– Masters Degree in Psychology

– Strong background in social psychology, experimental research design, and statistics

– Candidates should be skilled at research methods, programming (Inquisit, QualtricsLime Survey or SoSciSurvey  Choreographe, etc.), development of experimental designs for lab and online research, and data analysis using R or SPSS

Other elements (not requirements):

– Applicants should have a strong interest in social robotics, including psychological determinants of successful human-robot interaction

– Excellent command of English language in spoken and written form, ideally complemented by a willingness to learn German

BENEFICIARY: SSSA

OBJECTIVES: The project aim is threefold: to identify the design criteria necessary for developing pleasant robots; to discuss the problem of deception and the possibility that (especially vulnerable) users may be lead to attribute features and qualities to the machine that, instead, machines do not possess (e.g. ability to feel emotions and build emotional bond with humans etc.); to discuss ethical and legal aspects of such interaction. For this matter, ESR14 will take into consideration the technological, philosophical and psychological research carried out at other partner institutions (also participating in said research, thanks to the exchange periods of  the duration of up to 6 months duration, over the three year program), the European ethical and legal principles (dignity, equality, self-determination), including the initiatives on the development of ethical guidelines for trustworthy AI (and advanced robotics) currently undertaken at the European level. In particular, the project will consider the challenges of standardization in ethics, considering alternative ethical approaches, such as the utilitarian and deontological (neo-kantian).

EXPECTED RESULTS: Analysis of the legal, social and ethical problems connected to the human machine interaction, identification of criteria for the development of the ethically aligned design; elaboration of policy guidelines on the topic; assessment of how fundamental principles and rights may be interpreted and applied to determine licit, and socially desirable uses of said applications.

Requirements:
– Degree in law (5 years or equivalent, id est full degree in law, theoretically allowing for the practice of law in the country of origin/completion of studies) or Juris Doctor (JD for the US)
– Preference for candidates holding an LLM master of laws, post the completion of full cycle of legal studies or other Masters degree in law, philosophy, ethics, psychology or social science, engineering, robotics, it or any other relevant field

– A detailed (up to 50.000 characters, including spaces and essential bibliography) project description clarifying (i) how the candidate identifies the issue of human-machine interaction and deception in a broader social science perspective (including ethical, philosophical, psychological), in light of existing and reasonable foreseeable robotics and AI-based technologies (ii) what legal implications he/she identifies, (iii) how the existing legal system could tackle such matters, primarily at European level (necessary), but also in a comparative perspective. The candidate should highlight what tools he deems existent today (de iure condito) within the extant legal framework (including the normative framework as well as trends in the case law). A comparative analysis, taking into account multiple EU member states, the European legal framework, as well as other prominent jurisdictions, is welcomed and preferred. (iv) Identify (if existent) possible alternative policy models, already existing or that could be implemented. A solid, despite essential bibliography should be provided to ground the analysis presented.

Other elements (not requirements):

– Work experience in (i) legal practice/consulting, (ii) research, (iii) policy and government, not a requirement but to be evaluated by the selecting committee

– Master thesis if available

BENEFICIARY: UNIVIE

OBJECTIVES: The aim of the project is to define the user’s emotional perspective and her moral rights in care situations. First, the persuasive technologies and social robots and the discourses of critique and legitimation surrounding them. Second, the ethical questions regarding the role and tasks, moral agency and responsibility assigned to care and companion robots in the context of elderly care. Third, trust, deception and data privacy. Finally, the implications for policymaking considering that currently healthy persons pay into the social insurance system in the acceptance of being cared for by people.

EXPECTED RESULTS: The project delivers a case study for moral deliberation in research and engineering of “persuasive technologies” and social robots for the concrete use case of care and companion robots targeted on elderly people. It will insight for the elaboration of RRI guidelines with respect the moral implication of the use of assistive robotics.

Requirements:

– Background in philosophy and technology ethics, in particular ethics of robotics, and who is familiar with, and preferably has experience with, an interdisciplinary approach in this field