Post Doc - Machine Learning

We are looking for a dedicated and innovative postdoctoral fellow to focus on small molecule machine learning applications in analytical development. Your work will involve predicting properties relevant to analytical methods such as HPLC and MS, utilizing molecular machine learning techniques that map structure to properties.

This Position is limited for 2 years.

Tasks & responsibilities

• In your new role, you will develop and enhance machine learning models that predict chemical properties relevant to analytical method development.
• Moreover, you will utilize molecular machine learning techniques, deep learning and Bayesian Optimization.
• With your expertise, you will improve existing models and workflows while also contributing new ideas and methodologies.
• You will collaborate with data scientists, software engineers, and domain experts to integrate machine learning solutions into analytical development processes.
• Furthermore, you will leverage both internal and external data sources to enrich model accuracy and applicability.
• You will also participate in a supportive and collaborative team environment, fostering continuous learning and professional growth.
• In addition, you will share your research outcomes through internal presentations, publications, and participation in international conferences.

Requirements

• Ph.D. in Computer Science, Natural Sciences, or a related field
• Experience in molecular machine learning, deep learning, Bayesian Optimization
• Proficiency in relevant programming languages (e.g., Python)
• Strong collaborative skills and the ability to work effectively within a multidisciplinary team
• Enthusiasm for learning and growing professionally in a supportive work environment
• Knowledge in chemistry is a plus
• Fluency in English

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