We are looking for PhD students at the University of Edinburgh for research focused on: Machine learning and optimization algorithms, Generative AI and artificial data, Privacy, fairness and explainability, Topological and Geometric data analysis and other similar areas.

We are excited to offer two fully-funded PhD positions at the University of Plymouth in the fields of Neuromorphic AI, Brain-Computer Interaction, Optimization, and Biosignal Analysis. The first project, 'Generative AI for image and video reconstructions from brain signals', aims to develop an explainable and trustworthy Generative AI pipeline system capable of decoding in real-time electroencephalography (EEG) brain signals of human participants while they are passively viewing or imagining visual content (faces, animals, etc) and of reconstructing this visual content with high fidelity. The second project, 'Enhancing AI Performance with AMD's Neural Processing Units', aims to maximize the potential of AMD’s cutting-edge Neural Processing Units (NPUs) in the area of computer vision. This project is partially funded by AMD and the successful candidate will collaborate with AMD researchers.

Two post-doctoral research positions are available in AI-inspired drug design in the Biomedical Computing Group headed by Professor Jagath Rajapakse at Nanyang Technological University, Singapore, for a period of three years starting from 1 July 2025. The project investigates the design of biologics (peptides and antibodies) as anticancer therapeutic agents by using eXplainable AI (XAI) and generative AI (genAI). First, we build predictive AI models such as large language models (LLM) for predicting binding affinities of biologics. Second, we use XAI approaches such as integrated gradients for identifying the features of predictive models and the mechanism of action of biologics. Third, using these features as constraints, we will use genAI techniques such as LLM and diffusion models to generate biologics with anti-cancer properties. The candidate will develop necessary predictive AI, XAI and genAI methods for design of anti-cancer biologics.

The Mnemosyne team of the Inria centre of the University of Bordeaux (France) is looking for a talented postdoctoral fellow with confirmed competences in the domain of Machine Learning for the development of a modeling framework of Metacognition. Metacognition is the cognitive process by which, instead of just learning to associate a response or a behavior with a situation, animals (and mainly primates) monitor the functioning (and particularly errors) of simple cognitive processes, learn to inhibit automatic responses and promote instead contextually appropriate behavioral rules. Better understanding and modeling this process is important for several reasons. In cognitive neuroscience, it paves the way to exploring higher cognitive functions like reasoning, imagination and other kinds of deliberation-based thoughts. In Artificial Intelligence, it stands on the same grounds as Generative AI and proposes different processes and algorithms that might remedy several weaknesses of GenAI and suggest innovative brain-inspired extensions. Located in Bordeaux (France), the role of the postdoctoral fellow to be recruited is to participate to a research program, under the following axes: Axis 1: Specification of Metacognition and its main computational mechanisms: Metacognition is generally described through three main mechanisms: (i) the possibility to monitor cues indicating difficulties in the process of problem solving (errors or conflicts between resources), in order to inhibit elementary default responses, (ii) working memory to keep in sustained activity the different aspects to be integrated (goals and subgoals, predictions, constraints) and (iii) cognitive flexibility corresponding to new goals and contextual rules that can be learned and integrated in the process of problem solving. Existing models (including from our team) indicate possible correspondence with cerebral circuitries and adaptive operations. Nevertheless, they are many and split these general mechanisms in different pieces which are not always consistent and may differ under several aspects. A major contribution will be to carry out a thorough analysis of these elements, to propose a synthesis associating both a precise description of the mechanisms and a map of their functional dependencies. Axis 2: Definition of relevant tasks in the domain of visual reasoning: Although many standard tasks have been defined and shared for simple sensorimotor control, it is not yet the case for cognitive control, generally corresponding to much more complex behaviors. A variety of tasks have been proposed in models evoked above but they differently integrate fundamental constituents such as hierarchical and temporal dependencies. In a similar view of standardization as in the axis above, the goal will be consequently to enumerate properties that have to be assessed when developing such metacognitive models and propose or design corresponding tasks. Subsequently, the postdoctoral fellow will work on integrating the insights from Axis 1 and task definitions in this Axis, with an architecture that integrates selected mechanisms from the different frameworks, particularly under the perspective of extending and evaluating models proposed in our team with novel properties. Axis 3: Organization of an international network of collaboration on the topic: We have already begun to identify and contact international (mainly European) teams working on the topic and willing to contribute to the elaboration of such a roadmap, toward more ambitious international projects. A corresponding goal will be to interact with these partners and to help with the preparation of such projects. This postdoc position is proposed for 18 to 24 months, preferably starting on November 1st, 2025 and will be located in the Mnemosyne team, in Bordeaux, France.

Generative AI is fundamentally transforming the software development industry by improving processes such as software testing, bug detection, bug fixes, and developer productivity. This talk explores how AI-driven techniques, particularly large language models (LLMs), are being utilized to generate realistic test scenarios, automate bug detection and repair, and streamline development workflows. As these technologies evolve, they promise to improve software quality and efficiency significantly. The discussion will cover key methodologies, challenges, and the future impact of generative AI on the software development lifecycle, offering a comprehensive overview of its revolutionary potential in the industry.