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Your Job: Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets Integrate ontologies and metadata (e.g., tissue, cell
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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring
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information about our institute here: https://www.fz-juelich.de/en/ias/ias-8 Your Job: Develop physics-aware simulations of growing cell populations, including their spatiotemporal manipulation in microfluidic
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mentoring, transferable-skills training, and funding for international conferences, research stays, and networking within the Helmholtz Association. Unique combination of science-to-impact opportunities
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(including data science courses, soft skill courses and annual retreats): https://www.hds-lee.de/about/ A qualification that is highly valued in industry 30 days of annual leave and flexible working
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large
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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only