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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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logical way and parametric simulation engines. Familiarity of machine learning / AI techniques and custom LLMs generation is beneficial. Knowledge of life cycle assessment (LCA) and LCA methods, carbon
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: experience working with spatial omics or spatially resolved data in tissue experience applying AI or machine learning methods to digital pathology or histology data experience analysing kidney or transplant
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(adaptive) imaging strategies and reconstruction. The research combines ultrasound physics, signal processing, machine learning, computational imaging, and clinical translation. Beyond your individual
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for highly motivated postdoctoral candidates with a PhD in bioengineering deep knowledge in computational biology and machine learning. Candidates with a molecular biology or engineering degrees with
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spans computational materials design, catalysis, energy materials, machine learning, and artificial intelligence. We offer a collaborative and international research environment with close interactions
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, No UMO-2020/39/I/NZ5/03104, Dissecting cancer activome to develop new generation of antibody-drug conjugates, PI: PhD. Eng. DSc. Marcin Poręba, professor at WUST Type of position: research assistant
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Essential PhD in immunology, hematology, molecular biology, stem cell biology, gene therapy, or a related discipline, achieved as at the fund’s start date (September 7, 2026) Strong experience in CAR T-cell
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the production of the CBC catalog. The successful candidate is expected to have strong analytical skills and experience with signal processing, Bayesian statistics and machine learning. Exemplary
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold