筋生理学分野の原書を受講者が交代で講読する。筋生理学分野の基礎知識や専門用語の習得、および英文に慣れることが授業の目標である。原書はいくつかの候補の中から、受講者の興味や研究テーマを考慮して決定する。 Students take turns reading original books in the field of muscle physiology. The goal of this course is to acquire basic knowledge and specialized terminology in muscle physiology, as well as to become familiar with English texts. Original books are selected from several candidates considering the interests and research themes of the students.

筋生理学分野の原書を受講者が交代で講読する。筋生理学分野の基礎知識や専門用語の習得、および英文に慣れることが授業の目標である。原書はいくつかの候補の中から、受講者の興味や研究テーマを考慮して決定する。 Students take turns reading original books in the field of muscle physiology. The goal of this course is to acquire basic knowledge and specialized terminology in muscle physiology, as well as to become familiar with English texts. Original books are selected from several candidates considering the interests and research themes of the students.

Statistics and biometrics have emerged as crucial disciplines not only in agricultural sciences but also in various other fields. This significance is primarily attributed to three factors: Firstly, advancements in data measurement techniques have facilitated the collection of extensive and diverse biological and agronomic data that were previously unattainable. Secondly, the evolution of data science methodologies has enabled the integration and modeling of such collected data. Thirdly, the enhancement of computational capabilities has empowered the utilization of these methodologies. These advancements have rendered statistical and biometric methods indispensable for extracting insights from the vast and varied biological and agronomic datasets. Throughout this lecture series, a diverse array of biological and agronomic datasets will serve as illustrative examples to demonstrate various analytical methods. Delivered in a hands-on format, utilizing R, Python, and Matlab, the aim is to equip students with practical analysis skills. The initial portion of the course, spanning the first one-third, will focus primarily on techniques for summarizing, visualizing, and modeling relationships within multivariate datasets. In the subsequent one-third, students will delve into linear models, linear mixed models, local regression, and nonlinear models. Finally, in the last segment, students will explore image analysis, machine learning, and deep learning methods. While the course will cover a broad spectrum of methods, ranging from introductory to advanced levels, the emphasis will be on developing the capability to independently conduct analyses rather than on elaborating on the theoretical underpinnings of the methods.

Students will explore and debate questions about human experience from multiple perspectives as they discover influential works from cultures around the world, both ancient and modern. Students will develop critical approaches and analytical skills as they exchange ideas with peers, ask questions, and respond to works in different genres through a variety of expressive modes. The course will focus on four fundamental themes: love, identity, death, and the meaning of life.

Students will explore and debate questions about human experience from multiple perspectives as they discover influential works from cultures around the world, both ancient and modern. Students will develop critical approaches and analytical skills as they exchange ideas with peers, ask questions, and respond to works in different genres through a variety of expressive modes. The course will focus on four fundamental themes: love, identity, death, and the meaning of life.

ゲノム研究の進展により生命科学は仮説駆動型からデータ駆動型の科学に変貌しつつある。このような科学を推進するには、多種多様で複雑なデータや知識を計算機でうまく扱えるようにすることが不可欠である。本講義では、その基礎となる理論や技術について解説する。 With the advancement of genome research, life science is transforming from a hypothesis-driven to a data-driven science. To promote such science, it is essential for computers to be able to successfully handle a wide variety of complex data and knowledge. In this lecture, the underlying theories and techniques will be explained.

At the end of the course, the student should have familiarised with the key concepts informing epistemological and methodological debates in social science, such as hypothesis, theory, falsification, verification, covering law, causality, with particular consideration for positivistic paradigms and anti- or post-positivistic reactions. The student should be aware of the specific difficulties of social scientific disciplines in studying human societies arising from the problematic separation between the studied object and the studying subject, and the link between social science and the political dimension. He should familiarise with problems of social research design, starting from the specificities characterizing the various stages of social science research. The student should become aware of the functional classification of social science methods in qualitative and quantitative, familiarising with a number of techniques in both domains, while grasping that contemporary social research increasingly requires the application of different methods and interdisciplinary approaches, which also lead to the necessity of teamwork coordination. The course also aims at providing a large variety of examples where the concepts and principles described above find their application and didactic clarification. Those examples will focus in particular on the study of technology and its social impact. Finally, the student should become aware of the moral implications, limits and rules informing social science research, as well as of the role and importance of effective communication in the social sciences.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the Science, Technology, Engineering and Mathematics (STEM) fields. The course will begin by addressing our own unconscious biases and stereotypes, and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. Students will be expected to read scientific articles, discuss them, and apply those concepts to examine their own social environments.

The course will take an intersectional approach to why we commonly find an underrepresentation of women in the Science, Technology, Engineering and Mathematics (STEM) fields. The course will begin by addressing our own unconscious biases and stereotypes, and question the research behind male and female differences. We will cover the brief history of the underrepresentation of minorities and explore research articles on why and how women and other minorities have been excluded from STEM fields. Students will be expected to read scientific articles, discuss them, and apply those concepts to examine their own social environments.

広く産学官にわたってグローバルに活躍するために必要な「俯瞰力」を養成することを目指す。物質科学の各分野について最先端の知識を修得し、自分の専門分野と周辺分野がどのように関連するか、あるいはし得るか、について深く考察するために、第一線で活躍する講師の方々にその分野の最前線を概観していただく。さらに、それらの講義を通して異分野間のコミュニケーションを円滑に進めるための具体的方法論を学ぶ。 This survey course is designed to enable students to develop the broad perspective that is required of global leaders working in and across industry, academia, and government. Students will gain knowledge and insight on advancements in each field of materials science research, given by leading researchers working on the frontline in those fields. This will allow students to consider how peripheral fields are related to their own area of expertise, and to consider the potential for forging bridges between related fields in the future. In addition, students will learn specific methodologies designed to facilitate smooth communication among different disciplines.