Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
sensors or inductive sensors, and demonstrate temperature readout of magnetic nano-objects engineered with high thermosensitivity. For the past 6 years, our team has been developing instrumentation
-
, files/database structures, data transformation, algorithms, and data output by using appropriate computer language/tools to provide technical solution for application development tasks. Assist with
-
-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms
-
technologies, ethical implications, and governance frameworks, including knowledge of algorithmic accountability and transparency. Experience with both qualitative and quantitative research methods, and
-
compare their systems using standardized metrics. Analyze sensor data and draw suitable conclusions for improving the robot's efficiency Conduct research to improve and innovate robotic technologies
-
/change data input, files/database structures, data transformation, algorithms, and data output by using appropriate computer language/tools to provide technical solutions for highly complex application
-
postdoctoral associate position in the experimental physics group of Prof. Alex Sushkov. The group's research is focused on precision experiments in the fields of fundamental physics, nuclear magnetic resonance
-
will join the Physics of Learning research group led by profs B. Ménard and M. Wyart. This group will significantly expand in the next couple of years, with the addition of several new faculty members
-
engineering systems analysis/optimization, or computational algorithms § Familiarity with multiple computer languages, including Python. § Ability to develop prototypes of tools needed to analyze data. Â
-
to develop algorithms that can extract valuable insights from large datasets. - Contribute to the development and improvement of flow cytometry techniques by utilizing computational statistics and machine