Toyota Motor Corporation (Switzerland)
In research, Toyota Motor Corporation (Switzerland) stands highest in Physical Sciences (#6,458 of 12,888 worldwide) and Engineering (#4,014 of 6,636 worldwide), 2022–2025. Relative to its size it is most specialised in Automotive Engineering and Mechanical Engineering — Automotive Engineering is 10.3× its share of world research.
- World rank, 2022–2025
- #11,259 of 28,054 · #4,571 all time
- Rank in Switzerland
- #67 of 220
- Research works
- 3.5k ▼ 27% vs 2013–17
- Citations
- 49k 13.7 per fractional work
- Top-10% rate
- 7.8% record average 16.5%
- Open access
- 49% world 28%
What is Toyota Motor Corporation (Switzerland) known for in research?
The fields where it stands highest, 2022–2025, ranked among every institution above the floor in each field.
| Field | World rank | Where that sits | Top-10% rate | Works | All time |
|---|---|---|---|---|---|
| Physical SciencesDomain | #6,458 of 12,888 | 9.2% | 297 | #2862 | |
| EngineeringField | #4,014 of 6,636 | 8.8% | 179 | #1971 |
Each strip is that field’s whole ranked pool, with the notch where Toyota Motor Corporation (Switzerland) sits in it, in the colour of the band that rank falls in. The track under the rate is the rate itself: it carries no world mark, because the world rate differs by field (from about 6% to 21% in this record).
What does Toyota Motor Corporation (Switzerland) specialise in?
Where its research is concentrated relative to its size: Automotive Engineering takes 10.3× the share of its output that it takes of world research.
- Automotive EngineeringSubfield · 23.0 works10×
- Mechanical EngineeringSubfield · 26.8 works3.5×
Location quotient, a volume reading rather than an impact one. It surfaces small, lopsided specialities.
Field profile
Location quotient across every field it publishes in: outside the ring is more than an institution of this size would be expected to publish, inside it is less.
Rings at 0.5×, 1× and 2×. Widest outward: Engineering, 2.9×. Every wedge is a field page.
- Engineering 2.9×
- Materials Science 2.3×
- Energy 2.2×
- Chemical Engineering 2.2×
- Computer Science 1.7×
- Health Professions 1.3×
- Decision Sciences 0.8×
- Physics and Astronomy 0.8×
- Psychology 0.8×
- Immunology and Microbiology 0.7×
- Neuroscience 0.6×
- Arts and Humanities 0.6×
- Biochemistry, Genetics and Molecular Biology 0.5×
- Chemistry 0.5×
- Medicine 0.5×
- Social Sciences 0.4×
- Business, Management and Accounting 0.4×
- Economics, Econometrics and Finance 0.3×
- Mathematics 0.3×
- Earth and Planetary Sciences 0.3×
- Environmental Science 0.3×
- Agricultural and Biological Sciences 0.2×
- Pharmacology, Toxicology and Pharmaceutics 0.1×
- Dentistry 0.1×
Who are the top researchers at Toyota Motor Corporation (Switzerland)?
Ranked on the composite score, Eiichi Ōno, Masami Iwamoto and Kazuo MIKI lead among researchers whose main affiliation is Toyota Motor Corporation (Switzerland).
- 1 Eiichi Ōno Switzerland · #329,715 worldwide 76 citations · 41 works
- 2 Masami Iwamoto Switzerland · #338,998 worldwide 51 citations · 28 works
- 3 Kazuo MIKI Switzerland · #561,248 worldwide 32 citations · 20 works
- 4 Yuko Nakahira Switzerland · #745,899 worldwide 20 citations · 15 works
- 5 Tsuyoshi Nomura Switzerland · #814,319 worldwide 127 citations · 100 works
- 6 Takashi Naito Switzerland · #848,100 worldwide 47 citations · 19 works
- 7 Shin Tajima Switzerland · #967,673 worldwide 26 citations · 21 works
- 8 Hideki Sugiura Switzerland · #1,032,314 worldwide 16 citations · 22 works
- 9 Masahiro Sugiura Switzerland · #1,141,228 worldwide 70 citations · 16 works
- 10 Tsuyoshi Sasaki Switzerland · #1,159,876 worldwide 64 citations · 40 works
Which keywords describe research at Toyota Motor Corporation (Switzerland)?
By fractional works in 2022–2025, weighted toward what it does more of than the world: Deep Learning, Autonomous Vehicles, Electric Vehicles, Neural Networks, Machine Learning, Polymer Electrolyte Membranes, Proton Exchange Membranes and Mechanical Properties.
- Trajectory Prediction
- Thermal Runaway
- Vehicle Dynamics
- Random Projections
- Convex Optimization
- Sensor Fusion
- Intelligent Transportation Systems
- Teaching Methods
- Digital Education
- Cathode Materials
- Collision Avoidance
- Materials Informatics
- Computational Chemistry
- Model Predictive Control
- Control Systems
- Data Mining
- Polymer Electrolyte Membranes
- Sustainability
- Mechanical Properties
- Machine Learning
- Deep Learning
- Neural Networks
- Autonomous Vehicles
- Electric Vehicles
- Proton Exchange Membranes
- Lithium-ion Batteries
- Quantum Mechanics
- Molecular Dynamics
- High-Throughput
- Power Electronics
- Thermal Management
- Microstructure
- Pedagogy
- Additive Manufacturing
- Renewable Energy Integration
- Catalysts
- Vibration Control
- Stochastic Gradient Descent
- Battery Management Systems
- Driver Assistance Systems
Size is fractional works in 2022–2025 in the topics tagged with each word; colour is the word's share of this institution's work against its share of the world's. The 40 words are chosen for being large and distinctive. Each links to the topic it comes from most.
All 40 words, with their numbers
- Deep Learning34▲ 2.4×48 topics
- Machine Learning24▲ 1.78×39 topics
- Neural Networks19▲ 2.9×25 topics
- Mechanical Properties15▲ 3.7×21 topics
- Autonomous Vehicles13▲ 15×4 topics
- Sustainability13▲ 1.29×16 topics
- Electric Vehicles13▲ 11×4 topics
- Polymer Electrolyte Membranes11▲ 34×1 topic
- Proton Exchange Membranes11▲ 34×1 topic
- Data Mining11▲ 3.4×9 topics
- Lithium-ion Batteries9▲ 5.3×3 topics
- Control Systems9▲ 12×5 topics
- Quantum Mechanics9▲ 27×4 topics
- Model Predictive Control9▲ 8.5×5 topics
- Molecular Dynamics9▲ 13×3 topics
- Computational Chemistry9▲ 29×2 topics
- High-Throughput8▲ 38×1 topic
- Materials Informatics8▲ 38×1 topic
- Power Electronics8▲ 5.8×7 topics
- Collision Avoidance8▲ 10×3 topics
- Thermal Management8▲ 5.7×7 topics
- Cathode Materials8▲ 4.6×3 topics
- Microstructure7▲ 3.4×9 topics
- Digital Education7▲ 13×2 topics
- Pedagogy7▲ 3.5×1 topic
- Teaching Methods7▲ 12×1 topic
- Additive Manufacturing7▲ 5.1×7 topics
- Intelligent Transportation Systems6▲ 10×3 topics
- Renewable Energy Integration6▲ 4.6×5 topics
- Sensor Fusion6▲ 13×3 topics
- Catalysts6▲ 7.2×3 topics
- Convex Optimization6▲ 18×2 topics
- Vibration Control6▲ 22×3 topics
- Random Projections5▲ 85×1 topic
- Stochastic Gradient Descent5▲ 85×1 topic
- Vehicle Dynamics5▲ 21×2 topics
- Battery Management Systems5▲ 7.8×1 topic
- Thermal Runaway5▲ 7.8×1 topic
- Driver Assistance Systems5▲ 20×1 topic
- Trajectory Prediction5▲ 20×1 topic
Which research topics does Toyota Motor Corporation (Switzerland) publish most on?
By volume in 2022–2025: Fuel Cells and Related Materials, Machine Learning in Materials Science, Education, Innovation and Language Studies and Stochastic Gradient Optimization Techniques.
Area is fractional works; colour is the subfield each topic belongs to.
- 1 Fuel Cells and Related Materials Electrical and Electronic Engineering 11 works
- 2 Machine Learning in Materials Science Materials Chemistry 8 works
- 3 Education, Innovation and Language Studies Education 7 works
- 4 Stochastic Gradient Optimization Techniques Artificial Intelligence 5 works
- 5 Advanced Battery Technologies Research Automotive Engineering 5 works
- 6 Autonomous Vehicle Technology and Safety Automotive Engineering 5 works
- 7 Electrocatalysts for Energy Conversion Renewable Energy, Sustainability and the Environment 4 works
- 8 Aging, Elder Care, and Social Issues General Health Professions 4 works
- 9 Hermeneutics and Narrative Identity Philosophy 4 works
- 10 Health, Medicine and Society General Health Professions 4 works
How open and international is its research?
Against the world’s own shares — the tick on each track. Both are shares of its output, so they sit on one scale and can be read against each other as well as against the world.
World: 28% of research is openly available.
World: 19% is written across borders.
How has Toyota Motor Corporation (Switzerland)'s research output changed?
Output in 2018–2022 was 27% lower than in 2013–2017.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Research measures only: rankings here say nothing about teaching, admissions or student experience. Comparable institutions and collaboration partners are in the interactive view on the map.