data-science

Bosch Group

<div><section><div><p></p><h2>Company Description</h2></div><div><p><strong>At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. </strong><strong>Join in and feel the difference.</strong></p><p>The Robert Bosch GmbH is looking forward to your application!</p></div></section><section><div><p></p><h2>Job Description</h2></div><div><ul><li>Normalizing flows represent a very promising modeling choice for counterfactual analysis, mainly due to the two-way (e.g., invertible) continuous and differentiable mapping from the observation to the latent space.</li><li>Such models, however, drastically fail due to an ill conditioned Jacobian when trying to model low dimensional data manifolds that are embedded in a higher dimensional space (see [1]).</li><li>The objective of this thesis work is to find practical solutions addressing that limitation.</li></ul></div></section><section><div><p></p><h2>Qualifications</h2></div><div><ul><li><strong>Education: </strong>Master<strong> </strong>studies in the field of in Data Science, Machine Learning, Computer Science, Cybernetics, Electrical Engineering, Mathematics or comparable</li><li><strong>Personality and Working Practice:</strong> independent, goal-oriented, reliable and work-oriented</li><li><strong>Experience and Knowledge:</strong> experienced in Python or Matlab, mathematical knowledge as well as fundamental research in machine learning<strong> </strong></li></ul></div></section><section><div><p></p><h2>Additional Information</h2></div><div><p><strong>Start: </strong>according to prior agreement<br>
<strong>Duration:</strong> 6 months</p><p>Requirement for this thesis is the enrollment at university. Please attach a motivation letter, your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.</p><p><strong>Need further information about the job?</strong><br>
Karim Barsim (Business Department)<br>
+49 174 3650742</p><p>Apply now in just 3 minutes!</p><p><a href="https://proceedings.neurips.cc/paper/2021/hash/4c07fe24771249c343e70c32289c1192-Abstract.html">[1] </a></p></div></section></div>

Thesis Normalizing Flows on low dimensional Data Manifolds

remote

5 months ago