Deep Learning jobs

Three-year salaried doctoral appointment studying the expressivity, robustness and verification of neural models of automata. The project connects systems theory, statistics, optimization and formal methods, examining recurrent networks, attention mechanisms and state-space models. Work includes theoretical analysis, experimental algorithms and scientific…

Investigate foundation models for safe, socially aware human–robot interaction in the HUCEBOT team. The ENACT-funded project combines vision-language and vision-language-action models with motion planning and control, addressing hallucinations, long-horizon behavior, human communication and environmental adaptability. Socially aware robot navigation is the…

Postdoctorat de 36 mois à Grenoble dans le projet ANR BEAUT, à partir du 1er mars 2027. Les travaux exploitent les collisions plomb-plomb du Run 3 d’ALICE au LHC pour déterminer le transport des quarks lourds dans le plasma de quarks et de gluons. La personne recrutée développera un étiqueteur de jets de beauté par apprentissage automatique, mesurera leur…

Develop a Python library for evaluating theoretical generalization bounds in deep learning within the SIERRA team and ERC DYNASTY project. The role connects learning theory with practical neural-network evaluation through modular scientific software, empirical experiments and publications. The two-year appointment has a planned start of 1 December 2026.…

Develop scientific AI methods for understanding and predicting plant-community dynamics in space and time within the PEPR DynaBiod programme. Process-informed neural networks will combine data with ecological mechanisms, allowing inference from incomplete, heterogeneous and noisy observations. The project examines mathematical formulations, learning…

Lead the language-model research strand of the BELLE project in the ARGO team. Work covers evaluation and improvement of AI-generated formal proofs, expert preference datasets, Lean/Mathlib code quality, reinforcement learning and translation between proof assistants. Responsibilities include research leadership, supervision and reproducible open-source…

VIB’s Center for AI and Computational Biology and KU Leuven’s Department of Human Genetics seek a group leader and professor developing AI and mechanistic mathematical models for biological questions. Potential themes include hybrid mechanistic and deep-learning models, genome-regulation foundation models, single-cell and spatial multi-omics, protein…

VIB.AI and KU Leuven seek a group leader and professor developing AI and mechanistic models for biological research. Possible areas include genome-regulation foundation models, protein structure and interactions, cellular imaging, disease models, digital twins and experimental feedback. The role combines independent research, supervision, teaching and…

Develop smartphone-based 3D capture and comparison methods for OnTheFly, a venture from Inria's GraphDeco team. Research focuses on aligning heterogeneous reconstructions, detecting changes between captures and reference models, and separating geometry, materials and lighting in Gaussian-splatting representations. The engineer will train and evaluate models…

Extend the KerData team's tool for analysing scientific papers and recommending experimental plans. Generalize its federated-learning-specific components, improve handling of long or irregular documents, evaluate extracted annotations against human-labelled data, and study retrieval and recommendation quality. The renewable four-month research-engineering…

Two funded postdoctoral appointments connect quantum information, many-body physics and machine learning with Yuxuan Zhang. Topics include verifiable quantum advantage, quantum simulation, neural quantum states and learning noise and control from device data. Appointments start in 2027, normally last two years initially and may be renewed. Researchers can…

Contrat doctoral de 36 mois à l’ENS de Lyon, début prévu le 1er janvier 2027, rémunéré 2 300 euros brut mensuel. Le projet PREBACTOR vise à comprendre la prédation bactérienne chez Myxococcus xanthus. La thèse construira un réseau d’interactions entre les protéines des appareils Kil et T3SS* avec des méthodes de prédiction structurale par réseaux de…

The Haibe-Kains Lab at Princess Margaret Cancer Centre seeks a researcher for computational oncology, biomarker discovery and treatment-response modelling. Work combines genomic, molecular and clinical data using multimodal learning and foundation models, with reproducible, privacy-aware and federated workflows across collaborating institutions. The…

Research neural interfaces, implantable neurotechnology, neural decoding and closed-loop brain-computer interfaces. Topics include electrode and sensor design, biocompatible materials, high-channel-count recording and stimulation, neurophotonic probes combining optical stimulation with electrophysiology, and adaptive or deep-learning decoders. The…

Develop a framework for network tomography combining graph neural networks with large language models. The GENIE ANR project investigates generalization across unseen network topologies, inference of non-additive metrics, and Graph-RAG diagnostics grounded in incident records and routing information. The doctorate includes experimental validation on…

Develop pretrained models of functional, diffusion and structural MRI that predict individual cognition with calibrated uncertainty. The ANR-funded MIND–GIN Bordeaux collaboration combines preprocessing and harmonization of large datasets, generative and self-supervised models, and transfer to smaller cognitive-neuroscience studies. The doctorate will…

The Nirschl laboratory develops AI methods for human neurodegenerative disease. Projects combine whole-slide pathology, clinical and imaging records, molecular measurements and spatial omics to study tissue phenotypes and validate biomarkers in Alzheimer’s and Lewy body diseases. Responsibilities include model development and evaluation, reproducible…

Develop an independent research and teaching programme in fundamental machine learning. Relevant directions include post-transformer architectures, continual learning, probabilistic and causal models, uncertainty quantification, learning theory, symbolic-statistical methods, robust learning and scientific discovery. The appointee will supervise master’s and…

Join DukeNLP to study reliable language generation, model and agent behaviour over long tasks, and evaluation of AI-assisted work. Projects may address factual errors, transparency, provenance and the human effort needed to verify outputs. The researcher develops independent projects with Bhuwan Dhingra and collaborates with students. The initial…

Develop data-driven models of collective human behaviour in the VirtUs team’s FOUL-X project. Explore generative, imitation-learning and physics-informed approaches using real pedestrian trajectories, adapting models across sites and limited datasets. Work closely with the project’s data-acquisition researchers and evaluate simulation quality. The…

Develop AI-based software tools for materials research and scientific computing. The work explores how foundation models and generated code can improve research workflows and computational methods.

The A. Mathis Group for Computational Neuroscience and AI (CNAI), led by Professor Alexander Mathis, is recruiting two postdoctoral fellows. The group studies how to measure and understand behaviour with AI, how brains and embodied agents learn to control the body, and how the brain builds a sense of the body through proprioception; it develops open-source…

Develop deep-learning methods for generative protein design at the interface of machine learning, molecular simulation, structural biology and experimental protein engineering. Projects can investigate conformational ensembles, integrate protein language models with physics, or design proteins for challenging membrane and sugar interfaces. The researcher…

Develop machine-learning methods for quantitative neuropathology within the Brain Health initiative. Integrate histology, spatial molecular data, MRI and clinical information to investigate brain-disease mechanisms and progression. The role includes computer-vision models, multimodal learning, Bayesian and causal models, scientific collaboration and open…