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.

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.

マテリアルの機能（電気物性、光物性など）を理解するために必要不可欠な量子力学の基礎を学ぶ。量子力学的世界像が日常的感覚とどのように異なるかを理解し、マテリアル中での電子や原子の振舞いをイメージできるようになることをねらいとする。特に、対象とする問題に即してそこでの量子力学的特徴（古典力学との違い）を説明できること、自由電子モデルのシュレディンガー方程式を解くことができ、その結果の意味を説明できることを目標とする。／Fundamentals of quantum mechanics essential for understanding the properties of materials (electrical, optical, etc.) are explained. The goal is to understand how the quantum mechanical worldview differs from everyday intuition and to develop the ability to see the behavior of electrons and atoms within materials. Specifically, students should be able to explain the quantum mechanical characteristics (differences from classical mechanics) relevant to the problem at hand, solve the Schrödinger equation for the free electron model, and interpret the results.

量子コンピューターの研究は、微細加工技術が進歩し、情報処理を担う「素子」が原子レベルに近づきつつある今日、当然の流れと言えよう。「素子」が原子レベルに近づくと、その動作はニュートン力学ではなく量子力学という運動法則に支配される。そこでは、アインシュタインとボーアの論争に代表されるシュレーディンガーの猫状態やＥＰＲ相関（一種のテレパシー？？）が実際に起こる。量子コンピューターでは、これらの摩訶不思議な「量子効果」を用いて、情報のやり取りや情報処理を行う。この講義では、量子コンピューターの原理と現状についての入門的な講義を行う。

本講義では、機械学習と物性物理学の接点を、理論的構造に立ち返りながら体系的に解説する。 物性計算における代表的な問題（相の識別、量子多体系の基底状態、第一原理計算、モンテカルロ法）を題材とし、それらに対して機械学習がどのような役割を果たすのかを議論する。 機械学習を単なるツールとして紹介するのではなく、 「関数の構成（function construction）」と 「確率分布の構成（distribution construction）」 という二つの観点から整理し、物理法則・対称性・変分原理との関係を明確にする。 機械学習未経験者も受講可能とするが、線形代数および量子力学・統計力学の基礎知識を前提とする。 / This course provides a systematic introduction to the interface between machine learning and condensed matter physics, emphasizing underlying mathematical and physical structures. We examine central problems in computational condensed matter physics — phase identification, many-body ground states, electronic structure, and Monte Carlo sampling — and discuss how machine learning contributes to each of them. Rather than treating machine learning as a black-box tool, we organize the course around two central ideas: •Function construction •Distribution construction and clarify their connections to physical principles such as symmetry, variational methods, and conservation laws. No prior experience in machine learning is required. Basic knowledge of linear algebra, quantum mechanics, and statistical mechanics is assumed.

Human activities are driving rapid environmental change and accelerating species extinction rates, raising concerns that Earth may be entering a sixth mass extinction. To address today’s global ecological challenges and make informed decisions about biodiversity conservation, we must first understand how life has responded to major environmental changes in the prehistoric past. The fossil record provides the only direct evidence of past extinction events that have shaped the history of life. In this course, we will examine how the fossil record came to be and how we use multidisciplinary scientific methods to interpret it and reconstruct Earth’s history. We will explore how life and environments in the oceans and on land have changed throughout deep time, with particular emphasis on the causes and consequences of the "Big Five" and other past extinction events. We will also consider human origins, the environmental and ecological consequences of our ancestors' spread from Africa across the globe, and how human beings continue to reshape life on our planet in unprecedented ways. By tracing how life has endured and recovered from past episodes of climate change and extinction, we will discuss what the fossil record can teach us about the resilience of life and the future of biodiversity.

Human activities are driving rapid environmental change and accelerating species extinction rates, raising concerns that Earth may be entering a sixth mass extinction. To address today’s global ecological challenges and make informed decisions about biodiversity conservation, we must first understand how life has responded to major environmental changes in the prehistoric past. The fossil record provides the only direct evidence of past extinction events that have shaped the history of life. In this course, we will examine how the fossil record came to be and how we use multidisciplinary scientific methods to interpret it and reconstruct Earth’s history. We will explore how life and environments in the oceans and on land have changed throughout deep time, with particular emphasis on the causes and consequences of the "Big Five" and other past extinction events. We will also consider human origins, the environmental and ecological consequences of our ancestors' spread from Africa across the globe, and how human beings continue to reshape life on our planet in unprecedented ways. By tracing how life has endured and recovered from past episodes of climate change and extinction, we will discuss what the fossil record can teach us about the resilience of life and the future of biodiversity.

担当教員が最先端の研究成果について基礎事項を踏まえながら説明し、物性研究の最前線で何が問題となり、どこまで解決されているのかを解説する。 Six professors from ISSP will introduce the results of cutting-edge research in solid state physics. Students will learn about the latest problems in this field and the ongoing studies aimed at solving them.

オーストラリア国立大学(ANU)と進めている国際教育・研究の枠組みの中で行う授業。 全学の学生に開講する。”共生”をテーマにANUから学生が来日し合同でフィールドワークや実験、講義などを行うことにより、国際感覚も養いながら、科学についての理解を進める。/Joint course work with students at the Australian National University. If Covid-19 situation improve, we will host students from the Australian National University to study various topics including Geohazards, AI, Science communications, Philosophy and Science and others. Otherwise, online virtual joint course will be conducted. Theme of the course will be "Kyosei: Co-existence".