The internship aims to develop a controller for a social mobile robot to have a conversation with people using large language models (LLMs) such as ChatGPT. The internship is part of the European SPRING project, which aims to develop mobile robots for healthcare environments. The intern will develop a controller (Python, ROS) for ARI, the social robot. The controller will navigate towards a human (or group), have a conversation with them, and leave the conversation. The intern will use existing components from the SPRING project such as mapping and localization of the robot and humans, human-aware navigation, speech recognition, and a simple dialogue system based on ChatGPT. The intern will also investigate how to optimally use LLMs such as ChatGPT for natural and comfortable conversation with the robot, for example, by using prompt engineering. The intern will have the chance to develop and implement their own ideas to improve the conversation with the robot, for example, by investigating gaze, gestures, or emotions.

The AI Department of the Donders Centre for Cognition (DCC), embedded in the Donders Institute for Brain, Cognition and Behaviour, and the School of Artificial Intelligence at Radboud University Nijmegen are looking for a researcher in reinforcement learning with an emphasis on safety and robustness, an interest in natural computing as well as in applications in neurotechnology and other domains such as robotics, healthcare and/or sustainability. You will be expected to perform top-quality research in (deep) reinforcement learning, actively contribute to the DBI2 consortium, interact and collaborate with other researchers and specialists in academia and/or industry, and be an inspiring member of our staff with excellent communication skills. You are also expected to engage with students through teaching and master projects not exceeding 20% of your time.

We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

The College of Arts and Sciences and the Luddy School of Informatics, Computing, and Engineering at Indiana University Bloomington invite applications for multiple open-rank, tenured or tenure-track faculty positions in one or more of the following areas: artificial intelligence, human intelligence, and machine learning to begin in Fall 2025 or after. Appointments will be in one or more departments, including Cognitive Science, Computer Science, Informatics, Intelligent Systems Engineering, Mathematics, and Psychological and Brain Sciences. We encourage applications from scholars who apply interdisciplinary perspectives across these fields to a variety of domains, including cognitive science, computational social sciences, computer vision, education, engineering, healthcare, mathematics, natural language processing, neuroscience, psychology, robotics, virtual reality, and beyond. Reflecting IU’s strong tradition of interdisciplinary research, we encourage diverse perspectives and innovative research that may intersect with or extend beyond these areas. The positions are part of a new university-wide initiative that aims to transform our understanding of human and artificial intelligence, involving multiple departments and schools, as well as the new Luddy Artificial Intelligence Center.

This 4-year PhD position offers you the chance to work in an innovative interdisciplinary environment, collaborating on groundbreaking research at the frontier of healthcare and robotics. As a PhD fellow, you’ll play a central role in building a predictive, multi-scale model of human skeletal muscle. This model will simulate how motor units within muscles respond to neural signals discharged by spinal neurons and adapt structurally over time when subjected to specific physical strain regimens. Leveraging machine learning and statistical modeling, you’ll integrate data from in vivo and in vitro studies to accurately predict muscle remodelling. The model will be validated against data from both healthy participants and post-stroke patients following a targeted 12-week leg training protocol. Using advanced tools such as high-density electromyography, ultrasound, and robotic dynamometry, you'll bridge biomechanics, neurophysiology and robotics, driving novel insights in muscle modelling and rehabilitation.

Waiting is one of healthcare’s core experiences. It is there in the time it takes to access services; through the days, weeks, months or years needed for diagnoses; in the time that treatment takes; and in the elongated time-frames of recovery, relapse, remission and dying.Funded by the Wellcome Trust, our project opens up what it means to wait in and for healthcare by examining lived experiences, representations and histories of delayed and impeded time.In an era in which time is lived at increasingly different and complex tempos, Waiting Times looks to understand both the difficulties and vital significance of waiting for practices of care, offering a fundamental re-conceptualisation of the relation between time and care in contemporary thinking about health, illness, and wellbeing.

Epilepsy surgery is a safe but underutilised treatment for drug-resistant focal epilepsy. One challenge in the presurgical evaluation of patients with drug-resistant epilepsy are patients considered “MRI negative”, i.e. where a structural brain abnormality has not been identified on MRI. A major pathology in “MRI negative” patients is focal cortical dysplasia (FCD), where lesions are often small or subtle and easily missed by visual inspection. In recent years, there has been an explosion in artificial intelligence (AI) research in the field of healthcare. Automated FCD detection is an area where the application of AI may translate into significant improvements in the presurgical evaluation of patients with focal epilepsy. I will provide an overview of our automated FCD detection work, the Multicentre Epilepsy Lesion Detection (MELD) project and how AI algorithms are beginning to be integrated into epilepsy presurgical planning at Great Ormond Street Hospital and elsewhere around the world. Finally, I will discuss the challenges and future work required to bring AI to the forefront of care for patients with epilepsy.

Socially Assistive Robots (SARs) have shown great potential to help children in therapeutic and healthcare contexts. SARs have been used for companionship, learning enhancement, social and communication skills rehabilitation for children with special needs (e.g., autism), and mood improvement. Robots can be used as novel tools to assess and rehabilitate children’s communication skills and mental well-being by providing affordable and accessible therapeutic and mental health services. In this talk, I will present the various studies I have conducted during my PhD and at the Cambridge Affective Intelligence and Robotics Lab to explore how robots can help assess and rehabilitate children’s communication skills and mental well-being. More specifically, I will provide both quantitative and qualitative results and findings from (i) an exploratory study with children with autism and global developmental disorders to investigate the use of intelligent personal assistants in therapy; (ii) an empirical study involving children with and without language disorders interacting with a physical robot, a virtual agent, and a human counterpart to assess their linguistic skills; (iii) an 8-week longitudinal study involving children with autism and language disorders who interacted either with a physical or a virtual robot to rehabilitate their linguistic skills; and (iv) an empirical study to aid the assessment of mental well-being in children. These findings can inform and help the child-robot interaction community design and develop new adaptive robots to help assess and rehabilitate linguistic skills and mental well-being in children.

A single person can be the source of billions of data points, whether these are generated from everyday internet use, healthcare records, wearable sensors or participation in experimental research. This vast amount of data can be used to make predictions about people and systems: what is the probability this person will develop diabetes in the next year? Will commit a crime? Will be a good employee? Is of a particular ethnicity? Predictions are simply represented by a number, produced by an algorithm. A single number in itself is not biased. How that number was generated, interpreted and subsequently used are all processes deeply susceptible to human bias and prejudices. This session will explore a philosophical perspective of data ethics and discuss practical steps to reducing statistical bias. There will be opportunity in the last section of the session for attendees to discuss and troubleshoot ethical questions from their own analyses in a ‘Data Clinic’.

Scientists work in laboratories; comfortable spaces which we equip and configure to be ideal for our needs. The scientific paradigm has been adopted by clinicians, who run diagnostic tests and treatments in fully equipped hospital facilities. Yet advances in technology mean that that increasingly many functions of a laboratory can be compressed into miniature devices, or even into a smartphone app. This has the potential to be transformative for healthcare in developing nations, allowing complex tests and interventions to be made available in every village. In this talk, I will give two examples of this approach from my recent work. In the field of stroke rehabilitation, I will present basic research which we have conducted in animals over the last decade. This reveals new ways to intervene and strengthen surviving pathways, which can be deployed in cheap electronic devices to enhance functional recovery. In degenerative disease, we have used Bayesian statistical methods to improve an algorithm to measure how rapidly a subject can stop an action. We then implemented this on a portable device and on a smartphone app. The measurement obtained can act as a useful screen for Parkinson’s Disease. I conclude with an outlook for the future of this approach, and an invitation to those who would be interesting in collaborating in rolling it out to in African settings.

Mindsets are core assumptions about the nature and workings of things in the world that orient us to a particular set of attributions, expectations, and goals. Our study of mindsets is, in part, inspired by research on the placebo effect, a robust demonstration of the ability of mindsets, conscious or subconscious, to elicit physiological changes in the body. This talk will explore the role of mindsets in three stages of chronic disease progression: genetic predisposition, behavioral prevention, and clinical treatment. I will discuss the mechanisms through which mindsets influence health as well as the myriad ways that mindsets can be more effectively leveraged to motivate healthy behaviors and improve 21st century healthcare.

The European University for Brain and Technology, NeurotechEU, is opening its doors on the 16th of December. From health & healthcare to learning & education, Neuroscience has a key role in addressing some of the most pressing challenges that we face in Europe today. Whether the challenge is the translation of fundamental research to advance the state of the art in prevention, diagnosis or treatment of brain disorders or explaining the complex interactions between the brain, individuals and their environments to design novel practices in cities, schools, hospitals, or companies, brain research is already providing solutions for society at large. There has never been a branch of study that is as inter- and multi-disciplinary as Neuroscience. From the humanities, social sciences and law to natural sciences, engineering and mathematics all traditional disciplines in modern universities have an interest in brain and behaviour as a subject matter. Neuroscience has a great promise to become an applied science, to provide brain-centred or brain-inspired solutions that could benefit the society and kindle a new economy in Europe. The European University of Brain and Technology (NeurotechEU) aims to be the backbone of this new vision by bringing together eight leading universities, 250+ partner research institutions, companies, societal stakeholders, cities, and non-governmental organizations to shape education and training for all segments of society and in all regions of Europe. We will educate students across all levels (bachelor’s, master’s, doctoral as well as life-long learners) and train the next generation multidisciplinary scientists, scholars and graduates, provide them direct access to cutting-edge infrastructure for fundamental, translational and applied research to help Europe address this unmet challenge.

COVID -19 has affected social interaction and healthcare worldwide. This talk will focus on the impact of the pandemic and “lockdown” on mental health services, community physical health services, and patient mortality in Cambridgeshire and Peterborough, based on the analysis of de-identified data from the primary NHS provider of secondary care mental health services to this population (~0.86 million)