Student / Research Assistant (f/m/x) - High- Throughput Effect-Directed Analysis (HT-EDA) in the field of Environmental Toxicology

The UFZ

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The job

Effect-directed analysis has become more efficient through automated and miniaturized fractionation and optimized bioassays in recent years. These advances have allowed researchers to identify new chemical compounds that risk aquatic environments using high-resolution mass spectrometry, liquid chromatography, and non-target screening. Despite these advances, two main challenges remain: first, identifying which features to prioritize for identification among thousands of potential candidates, and, second, determining the most likely structure of the selected features using in-silico fragmentation tools that can generate hundreds of possibilities based on the assigned molecular formula. Several computational tools that use machine learning to predict chemical activity based on available information, such as ms2 spectra, ionization behavior, and candidate structures, have been developed. These tools have the potential to enhance toxicity driver prioritization and toxicological endpoint prediction greatly and have yet to be explored in the context of EDA.

Your tasks

Aim: To implement and integrate promising computational tools to refine compound identification workflows within HT-EDA applications, thereby improving toxicity driver compounds' prioritization and identification processes.

Objective – Acitivity 2: Utilize computational approaches to link structures in the NORMAN Suspect List with potential effects on gene/pathway levels for environmentally relevant endpoints suitable for EDA studies, such as estrogenicity, androgenicity, and neurotoxicity, leveraging the deepFPlearn tool.

Key Responsibilities:

1. Literature Review & Tool Familiarization:

• Review existing literature on the deepFPlearn and the deepchem Python packages
• Understand the NORMAN Suspect List and environmental endpoints relevant to EDA studies
• Learn to operate the Python packages for computational analysis

2. Computational Analysis:

• Input the NORMAN Suspect List into deepFPlearn for computational association with environmental endpoints
• Analyze compounds for potential effects on gene/pathway levels, focusing on estrogenicity, androgenicity, and neurotoxicity

3. Data Interpretation & Reporting:

• Interpret computational findings to identify significant patterns or insights
• Draft a preliminary report on methodologies, results, and their implications for HT-EDA
• Collaborate on the preparation of findings for potential academic dissemination

We offer

• Excellent supervision that supports your personal and professional development
• Exciting insights into the work of a leading research institute
• The chance to work in interdisciplinary, international teams and benefit from a wide range of perspectives
• The opportunity to contribute and actively shape your own ideas and impulses right from the start
• Modern technical equipment and IT service to optimally support your work

Your profile

• Enrolled in or recently graduated (B.Sc. or M.Sc.) from a program in environmental science, toxicology, chemistry, bioinformatics, data/computer science, or a related field
• Interest in computational toxicology and environmental protection
• Experience with computational analysis in Python

Diversity and Inclusion

The UFZ has a strong commitment to diversity and actively supports equal opportunities for all employees regardless of their origin, religion, ideology, disability, age or sexual identity. We look forward to applications from people who are open-minded and enjoy working in diverse teams.

Nuestros anuncios de empleo se dirigen siempre a todas las personas profesionalmente capacitadas, independientemente de su edad, sexo, origen, orientación sexual, discapacidad, religión, ideología, etc. La selección de los candidatos está orientada exclusivamente a la cualificación.Se reservan los errores de información y ortografía.