Sort by
Refine Your Search
-
18.02.2025, Wissenschaftliches Personal The aim of the HerbiSens project is to identify new herbicidal compounds from natural resources. To this end, state-of-the-art analytical methods (e.g. LC-MS
-
, Management Science, Business Analytics, or a related field. Strong analytical skills with experience in AI, machine learning, or data analytics. Very good English skills in writing and communication
-
, such as participant recruitment or data collection, is not required but would be an advantage. All applicants must have strong analytical skills and a proven interest in interdisciplinary and teamwork
-
. As part of the SPP, the SoilEnergySpots project is jointly led by Prof. Dr. Nicole Strittmatter (Analytical Chemistry), Prof. Dr. Mirjana Minceva (Biothermodynamics), and Dr. Steffen Schweizer (Soil
-
ecology (e.g. pollination, chemical or molecular ecology). • Strong experience with statistical data analyses. • Experience in or willingness to learn analytical chemical analyses and data processing
-
disciplines - strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, sta-tistics, machine learning) - a high motivation and the ability to work independently
-
11.11.2024, Wissenschaftliches Personal In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the
-
stability assessment of novel catalysts by electrochemical flow cell measurements that will be coupled to on-line analytics (c.f. https://www.nature.com/articles/s41563-019-0555-5). Specifically, an in-house
-
, analytical chemistry •Interest in working independently on scientific issues •Willingness to work in a team with academic partners •Good knowledge of German and English, both written and spoken Tasks
-
, environmental or natural resource economics) or related disciplines strong analytical (i.e. microeconomics, production or resource economics) and methodological skills with a focus on quantitative data analysis