67 deep-learning PhD positions at Università degli Studi di Napoli "Federico II" in Belgium

PhD student: deep learning in genomics to decode cellular identities in the human brain

Description The Yves Moreau and Stein Aerts research labs are looking for a shared PhD student to apply deep representation learning and large language models to decipher the genomic regulatory code

PhD Student Causal Machine Learning

-generating mechanism, integrating it with recent insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project

PhD position: Machine Learning & AI methods for solver acceleration and

within the IN-DEEP project you will be at the forefront of developingnew hybrid machine learning (ML) accelerated solvers. A fast-expandingarea of research is the application of ML techniques to predict

PhD student: Machine learning in plant regulatory genomics

RNA-Seq, ChIP/DAP-Seq protein-DNA interaction data, bulk, and single-cell ATAC-Seq) and the application of diverse supervised machine learning approaches (e.g., feature-based, deep learning, and

PhD candidate for Spatial multi-omics data integration

analysis. Profile Essential You obtained a Master's in Computer Science, Artificial Intelligence, Bioinformatics, Physics, Engineering, Bio-engineering, or equivalent. Background in AI (deep learning

PhD candidate at the intersection of Cell-Cell Communication models and Spatial Omics

systems biology Background in AI (deep learning, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Desirable but not

PhD Student Causal Machine Learning

insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project seeks to enhance the robustness and efficiency

PhD student Causal Machine Learning

insights from debiased machine learning and causal inference. Besides laying foundations for a novel paradigm for causal/statistical modeling, this project seeks to enhance the robustness and efficiency

PhD position quantitative in situ electron microscopy of metal halide perovskite materials

of applying the concept of deep learning. By training a convolutional neural network, in which detailed image simulations are used as an input, we expect to be able to reliably locate even the lightest atomic

PhD Scholarship: Speculative Architecture and Postdigital Media Ecologies

operate on time frames ranging from the instant to deep time, and span across scales from the microscopic to the planetary. The PhD research project Speculative Architecture and Postdigital Media Ecologies