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will have the opportunity to investigate innovative solutions using machine learning algorithms and predictive modelling techniques in the context of a collaborative project with Goodyear Luxembourg (one
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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms
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algorithms to analyze OMICS data (e.g., genome, transcriptome, proteome, microbiome) from patient samples and basic research perform single-cell RNA-Seq and spatial transcriptomics analysis apply artificial
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activity in ulcerative colitis patients with transcriptional changes in a longitudinal patient cohort, develop deconvolution algorithms, extract features from H&E sections etc. Bacterial metabolism and host
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top 10 IT security/crypto conferences) Strong mathematical and algorithmic CS background, economics/finance - a plus Good skills in programming and scripting languages Commitment, team working and a
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/non-convex optimization (Mandatory) Signal processing Computational electromagnetics focused on time domain algorithms (Mandatory) Programming skills in MATLAB (Mandatory) Good oral presentation skills
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within tissues using our in-house developed spatial transcriptomics-based technology (Spatial VDJ). Using established and newly developed algorithms, we map B cell evolution within tissues, including class
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mechanism. Recent developments in protein structure prediction and protein de novo design have opened new possibilities for probing such mechanisms. The project will seek to use existing algorithms to new
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implementing inversion algorithms, including a focus on the integration of spatially constrained regularization schemes. Collaborating with forward modeling experts to ensure seamless integration with a recently
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to develop a 3D-generative algorithm for pharmaceutical drug design by using or combining novel machine learning approaches? How would you integrate machine learning, physics-based methods in an early-stage