Weizmann Soft Matter And Biophysics Program
We offer many relevant courses at the interface of Physics, Chemistry, and Biology.
Lecturer : Prof. Eran Bouchbinder
The course will help students understand the essentials of modern continuum physics, with a focus on solid mechanics and within a thermodynamic perspective, and to formulate physical problems within this framework.
Soft condensed matter physics
Lecturer : Prof. Sam Safran
The students will understand how the fundamentals of statistical thermodynamics can be used to theoretically predict the properties of soft matter (phase separation, interfaces, capillarity, polymers, gels, charged systems, micelles, and membranes). While biological/biophysical motivation will be presented, the focus on the course will be on the fundamental physical concepts and theory.
From biomolecules to biosystems
Lecturer : Prof. Joel Stavans
Get a bird's eye view of a wide variety of topics in Biology from a quantitative viewpoint, ranging from Molecular to Systems Biology scales.
Basic concepts in complex systems
Lecturer : Prof. Efi Efrat, Dr. Hillel Aharoni
Upon completion of the course, students will demonstrate an understanding of the concepts and mechanisms underlying the behavior of complex systems and identify their role in various natural phenomena. Digest contemporary research papers, lectures, and seminars in complex systems, statistical physics, soft condensed matter, biological physics, and related fields.
Topics in physical chemistry and biophysics
Lecturer: Prof. Hagen Hofmann
The course is an introduction into the basic ideas in statistical thermodynamics. It is specifically designed for students in biology and biochemistry, but it will also refresh the knowledge of chemists. From the rules of probabilities over concepts such as entropy and free energy up to theories of protein folding, the lecture aims at providing basic knowledge in physical chemistry, useful sets of mathematical tools, and direct links to current topics in biophysics. Importantly, the lecture does not require a specific pre-knowledge in mathematics or physics.
Biology and Sustainability by the numbers
Lecturer: Prof. Ron Milo
Over the past decades, biology and environmental studies have evolved rapidly from descriptive, qualitative disciplines to more analytical, data-driven and quantitative fields. Our ability to collect numbers that describe the most basic processes around us has increased significantly, and simple calculations based on these data can provide important insights and enrich our scientific intuition. In this course, we develop the ability to do back of the envelope calculations through practicing examples of illuminating questions.
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Lecturer : Prof. Uri Alon
The human body is a wondrous system. It is able to maintain healthy function despite huge molecular and environmental variations. The circuits that enable it to function so robustly have specific fragilities that lead to diseases. This course will provide basic principles for understanding human physiological circuits and concepts for making sense of disease processes and their dynamics. The course will include guitar songs and other enjoyable methods to improve attention and learning.
Lecturer : Prof. Ariel Amir
The purposes of this course are to familiarize students with a broad range of examples where randomness plays a key role, develop an intuition for it, and get to the level where students may read a recent research paper on the subject and be able to understand the terminology, the context, and the tools used. This is, in a sense, the "organizing principle" behind the various parts of the course: in all of them, we are driven by applications where probability and statistical physics play a fundamental role and lead to exciting and often intriguing phenomena.
Lecturer: Prof. Gregory Falkovich
Students will be able to demonstrate knowledge of fluid mechanics, both on a conceptual and a technical level.
Introduction to Big Data and Machine Learning
Lecturer: Leon Anavy, Prof. Zohar Yakhini
Upon successful completion of this course, students should be able to:
- understand machine learning algorithms and apply them to data
- statistically assess observations in data, including correlations
- launch and use Big Data platforms to analyze large volumes of data
- understand and configure machine learning packages, including Deep Learning and SVM
- analyze large volumes of experimental data and present results
Lecturer: Prof. Gregory Falkovich