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chromatin conformation during wood development and drought stress in spruce/pine. - Building integrative analyses that link TE‑derived cis‑regulatory elements (TE‑CREs) to gene expression divergence within
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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particular 2D/3D radiation-hydrodynamic simulations using high-performance computing (HPC) and comparison to observational data. The positions are fixed-term for two years and start immediately, preferablyno
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that link TE‑derived cis‑regulatory elements (TE‑CREs) to gene expression divergence within and across species. - Implementing ATAC‑STARR‑seq to validate enhancer activity; mapping enhancer–promoter links via
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energy efficiency during the training process and (ii) science yield (sensitivity / resolution) during inference. A key aspect is to benefit from hybrid HPC + AI approaches within the workflows
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a postdoctoral fellow at the University. The individual cannot have held previous positions in the professional ranks. Preferred Qualifications Experience with NGS data analysis and Linux based HPC
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 20 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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approaches applied to different science domains, such as chemistry, materials, physics, imaging, drug discovery, climate/weather forecast, etc. Knowledge of implicit deep learning models (NERF, neural
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work
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-cell sequencing technologies to study mutagenesis and tumorigenesis. Our interests include the sex-specific molecular differences in the acquisition and maintenance of malignancies, the identification