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Stanford University
1.2
based on 2 Reviews
About Stanford University
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Stanford University Ratings
based on 2 reviews
Overall Rating
1.2/5
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Category Ratings
5.0
Company culture
4.9
Salary
4.9
Promotions
4.9
Work satisfaction
4.8
Job security
4.7
Work-life balance
1.2
Skill development
Stanford University is rated 1.2 out of 5 stars on AmbitionBox, based on 2 company reviews.This rating reflects a below average employee experience, indicating poor satisfaction with the company’s work culture, benefits, and career growth opportunities. AmbitionBox gathers authentic employee reviews and ratings, making it a trusted platform for job seekers and employees in India.
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Stanford University Reviews
Compare Stanford University with Similar Companies
Change Company | Change Company | Change Company | ||
|---|---|---|---|---|
Overall Rating | 1.2/5 based on 2 reviews | 4.0/5 based on 2 reviews | 4.0/5 based on 1 reviews | 3.8/5 based on 62 reviews |
Highly Rated for | Company culture Salary Promotions | Work-life balance Company culture | Work-life balance Job security Salary | No highly rated category |
Critically Rated for | Skill development | Skill development Promotions Work satisfaction | Promotions Skill development Company culture | Promotions Salary Skill development |
Rating by Women Employees | - no rating available | - no rating available | - no rating available | 3.5 Good rated by 13 women |
Rating by Men Employees | - no rating available | - no rating available | - no rating available | 3.9 Good rated by 39 men |
Job security | 4.8 Excellent | 3.0 Average | 5.0 Excellent | 3.3 Average |
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Stanford University Salaries
Stanford University salaries have received with an average score of 4.9 out of 5 by 2 employees.
Store Controller
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₹1.1 L/yr - ₹1.4 L/yr
Community Manager
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₹24.3 L/yr - ₹31 L/yr
Research Intern
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₹0.9 L/yr - ₹1.2 L/yr
Merchant Navy
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₹1.8 L/yr - ₹2.4 L/yr
Finance Manager
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₹9.9 L/yr - ₹12.7 L/yr
Project Coordinator
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₹7.6 L/yr - ₹9.7 L/yr
Team Lead
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₹9 L/yr - ₹11.5 L/yr
Senior Technical Lead
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₹18 L/yr - ₹23 L/yr
Computer Operator
(1 salaries)
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₹9 L/yr - ₹11.5 L/yr
Senior Accountant
(1 salaries)
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₹2.8 L/yr - ₹3.6 L/yr
Stanford University News
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Study Reveals Environmental Pollutants Accelerate Aging and Health Risks in U.S. Adults
- A recent study published in Aging-US reveals that environmental chemical exposures may significantly impact epigenetic aging, contributing to accelerated aging and health risks in U.S. adults.
- The research, based on data from the National Health and Nutrition Examination Survey (NHANES), indicates a profound link between exposure to harmful chemicals and biological aging.
- Conducted by a team from Stanford University, the study analyzed blood and urine samples from 2,346 adults to assess the effects of 64 different chemicals on the aging process.
- Cadmium, lead, and cotinine were identified as significant contributors to accelerated biological aging, with cadmium being particularly concerning due to its association with age-related diseases.
- The study also highlighted the detrimental effects of tobacco exposure, as indicated by elevated levels of cotinine correlating with premature aging.
- Lead exposure, known for its toxic properties, was found to expedite the aging process and increase vulnerability to chronic diseases like cardiovascular issues and neurological degeneration.
- Certain pollutants, such as PCBs and dioxins, exhibited potential inverse relationships with aging markers, raising complexities in understanding their health effects.
- The study's comprehensive approach to analyzing a wide range of environmental chemicals underscores the need for stronger public health interventions to reduce exposures and mitigate health risks.
- The research emphasizes the urgency for policymakers to implement stricter environmental regulations to combat the impact of toxic substances on aging and chronic disease susceptibility.
- By raising awareness of the interplay between environmental pollutants and health outcomes, the study advocates for a holistic approach to public health initiatives that consider both individual choices and environmental factors.
Bioengineer | 5 Mar, 2025
Exploring the Brain’s Sugar-Coated Defense Against Aging
- Stanford researchers have discovered the crucial role of sugars in brain aging, focusing on the glycocalyx's impact on cognitive function and decline.
- The glycocalyx, a protective sugar coating on cells, has been identified as a key player in cellular communication and protection.
- Research led by Sophia Shi at Stanford reveals age-related changes in the glycocalyx of brain cells, shedding light on aging and cognitive decline mechanisms.
- Alterations in the glycocalyx contribute to the weakening of the blood-brain barrier (BBB) and increased permeability with aging.
- Thinning of the glycocalyx is linked to enhanced BBB permeability, triggering neuroinflammatory responses and cognitive decline.
- Restoring essential mucins in aged mice improved BBB integrity, reduced neuroinflammation, and enhanced cognitive function.
- Insights from the study suggest that modulating glycan composition could be crucial in addressing brain aging and associated diseases.
- The research signifies a breakthrough in understanding the role of sugars in brain health, offering potential for innovative therapeutic interventions.
- The study emphasizes interdisciplinary collaboration and innovative approaches to biological research beyond proteins and DNA.
- Uncovering the secrets of the glycocalyx could lead to improved drug delivery systems for treating neurological disorders.
Bioengineer | 27 Feb, 2025
OctoTools: Stanford’s open-source framework optimizes LLM reasoning through modular tool orchestration
- OctoTools is an open-source agentic platform developed by scientists at Stanford University.
- It enhances large language models (LLMs) for reasoning tasks by breaking them down into subunits and incorporating tools.
- OctoTools outperforms classic prompting methods and other LLM application frameworks, making it promising for real-world use.
- It offers an agentic framework that can orchestrate multiple tools without the need for model fine-tuning, addressing challenges in tool selection and domain limitations.
VentureBeat | 27 Feb, 2025
Can a Powerful AI Model Be Built on a Budget?
- Researchers at Stanford and the University of Washington have developed the s1 model, showing that a powerful AI model can be built on a budget.
- The s1 model was trained using strategic data curation and supervised fine-tuning on a compact dataset.
- The fine-tuning process on 16 NVIDIA H100 GPUs cost less than $50, resulting in a model that outperformed OpenAI's o1-preview model on competitive math benchmarks.
- The research challenges the notion that state-of-the-art AI requires huge investments, highlighting the potential for democratizing AI model training.
Hackernoon | 26 Feb, 2025
New IP-Focused Altcoin Surges 164% in First Week Amid Launch of Research Collaboration With Stanford University
- The brand-new native token of a layer-1 blockchain focused on intellectual property (IP) has surged by 164% in its first week of existence.
- Story (IP) launched at $2.26 and is currently trading at $5.98, making it the 71st-largest crypto asset by market cap.
- Story aims to serve as a blockchain for IP rights holders to upload, set terms, and monetize intellectual property throughout its application ecosystem.
- Story has joined Stanford University's Future of Digital Currency Initiative (FDCI) for research collaboration to accelerate its roadmap and enhance blockchain execution environments.
Cryptonews | 22 Feb, 2025
SLAC to Pioneer Fusion Energy Target Technology in DOE’s Fusion Innovation Research Engine Collaboratives
- SLAC National Accelerator Laboratory collaborates with the U.S. Department of Energy on Fusion Innovation Research Engine (FIRE) projects to advance fusion energy technology, targeting inertial fusion energy systems and aiming to replicate stars' fusion processes on Earth.
- The $107 million funding for six projects underscores the U.S.' commitment to fusion energy research, with a focus on collaboration between research institutions and industry partners to overcome technological barriers in commercializing fusion energy.
- The Target Injector Nexus for Experimental Development (TINEX) Collaborative, led by General Atomics, includes partners like Lawrence Livermore National Laboratory and Stanford University to address challenges in inertial fusion energy.
- The research emphasizes the development of fusion fuel targets and the management of debris and extreme temperature resilience, with SLAC's expertise in high-energy density science contributing significantly.
- The collaboration aims to refine target tracking technology for precise laser hits, essential for sustaining fusion reactions, with involvement from an industrial council to ensure alignment with industry requirements.
- Shared knowledge through TINEX initiatives benefits industrial and academic sectors, facilitating advancements in fusion technologies and workforce development, potentially revolutionizing energy sustainability on a grid-scale.
- The research extends beyond fusion energy, offering insights into broader energy sustainability strategies, highlighting the socio-economic impact of scientific advancements in energy systems and innovation.
- SLAC's efforts signal profound changes in energy production and imply a redefinition of energy sustainability, aiming for cleaner energy sources and a shift towards a greener environment with renewed investment in fusion energy.
- The collaborative push towards fusion energy may lead to breakthroughs not only in physics but also in creating eco-friendly energy systems that can meet global energy demands sustainably.
- SLAC's venture into fusion energy represents a shift towards realizing a future where fusion energy is a practical and essential element in the global energy landscape, potentially influencing a wide array of scientific domains.
- The fusion energy revolution, supported by diverse expertise and strategic partnerships, aspires to revolutionize energy generation and pave the way for a sustainable future powered by clean and reliable energy.
Bioengineer | 20 Feb, 2025
NVIDIA and Arc Institute Unveil an AI Model to Predict DNA, RNA & Proteins
- California-based nonprofit Arc Institute and Stanford University, in collaboration with NVIDIA, have unveiled Evo 2, the largest publicly available AI model for genomic data.
- Evo 2 can predict and design the genetic code of DNA, RNA, and proteins for all domains of life.
- The model has been trained on nearly 9 trillion nucleotides, and it is fully open to accelerate biological complexity exploration.
- The AI model can be used in biomolecular research applications, including protein structure prediction, identifying novel molecules, and studying gene mutations.
Analyticsindiamag | 20 Feb, 2025
Stanford Researchers Introduced a Multi-Agent Reinforcement Learning Framework for Effective Social Deduction in AI Communication
- Researchers from Stanford University have introduced a multi-agent reinforcement learning framework for effective social deduction in AI communication.
- The framework addresses the challenge of developing AI agents capable of effective communication in multi-agent environments.
- The method leverages structured rewards and divides communication into listening and speaking, allowing AI agents to optimize their skills independently.
- Experimental results show that the trained AI models outperformed traditional reinforcement learning models and achieved higher win rates in social deduction games.
Marktechpost | 18 Feb, 2025
[R] SWE-agent is the new open-source SOTA on SWE-bench Lite
- SWE-agent is an open-source software engineering agent that adds new features like massively parallel runs, cloud-based deployment, and extensive configurability.
- The 1.0 release of SWE-agent also includes a new command line interface and utilities.
- Powered by the SWE-ReX package, SWE-agent now supports lightweight, sandboxed code execution with options for local Docker, AWS, and Modal deployments.
- Developed by Princeton University and Stanford, SWE-agent is completely open-source under the MIT license.
Reddit | 13 Feb, 2025
![[R] SWE-agent is the new open-source SOTA on SWE-bench Lite](https://rdwgroup.com/wp-content/uploads/2018/10/reddit2-800x450-1.png)
MBA grads are struggling to find work amid a white-collar hiring slowdown
- White-collar job hiring slowdown has hurt MBA graduates in schools such as Harvard, Yale and Stanford.
- Several reasons contribute to the slowdown in MBA hiring - However, they may require a higher salary than their peers at a time when companies are looking to downsize, save money, and invest in technology.
- BI observed job acceptance rates three months post-graduation at the top 15 business schools from US News and World Report - the class of 2024's job acceptance rate being the lowest in eight of the 10 schools.
- Consulting firms and Big Tech, the usual suspects for MBA hiring, are hiring fewer MBAs, as part of the overall hiring slowdown.
- Interest rates and investment in AI have led to slow hiring of MBAs, but healthcare, government, and hospitality sectors have been dominant.
- While Alyssa might expect the management sector to have more job opportunities, management roles have just about 9% higher job openings than in February 2020.
- Companies, who have already notably invested in AI, reduce their recruitment of workers and dedicate the funds towards the development of AI tools.
- However, with future Federal Reserve interest rate cuts, improved labor market conditions can occur for MBA graduates.
- An uptick in retirements could lead to a gap in the work force that AI likely won't be able to fill, making it easier for MBA graduates to find work.
- Companies will need to think about preserving a pipeline of new workers so that, when senior members need to be replaced, their leaders of the future are experienced.
Insider | 11 Feb, 2025
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