MDU Rohtak Teaching Recruitment 2025 📚 112 Assistant Professor vacancies 📝 Pay Scale: Level‑10 (₹57,700 – 1,82,400) 📅 Apply Online: March 11 – April 3, 2024 🔗 Apply now at the MDU recruitment portal! #MDURohtak# #AssistantProfessor# #GovtTeachingJobs#

Natalie Behague is an Assistant Professor at Dublin City University working in extremal and probabilistic combinatorics. A Cambridge and QMUL graduate, she resolved hypergraph saturation irregularities, classified semi-perfect 1-factorizations of hypercubes, co-developed common pairs of graphs via entropy, proved rainbow saturation is linear, and helped settle the Kim–Vu sandwich conjecture - advancing discrete mathematics with containers, flag algebras and probabilistic methods.

Su Dan is speaking at Money Frontier 2026. Professor Su Dan is Assistant Professor of Finance at Cheung Kong Graduate School of Business. He received his PhD in Finance from the University of Minnesota Twin Cities in 2022. His research focuses on macro finance, fintech, and international capital flows, and he has deep insight into industry compliance and ecosystem development across cryptocurrency, stablecoins, and cross-border finance. His research has been published in leading international academic journals including the Journal of International Economics, International Economic Review, Management Science, Journal of Financial Markets, and Oxford Bulletin of Economics and Statistics, and has been widely covered by Bloomberg, VOXCHINA, and other authoritative media. He has received multiple awards, including the Western Finance Association PhD Student Award for Outstanding Research, the Financial Management Association Best Paper Award, the China Financial Research Conference Best Paper Award in Corporate Finance, the China FinTech Research Conference Best Paper Award, the MLU Faculty of Economics and Business Best Paper Award, the Capital Market Research in the AI Era Annual Conference Best Paper and Best Discussant Awards, and the CFRN Young Scholar Workshop Best Paper Award. Join him at Money Frontier 2026. Get your tickets and learn more:

ATF Chief Counsel Robert Leider has submitted his resignation and will return to his role as Assistant Professor of Law at George Mason University’s Antonin Scalia Law School. We thank Mr. Leider for his service and wish him well in his return to academia. More at

Hair loss can occur for many different reasons. Learn important details from Assistant Professor of Dermatology Luiza Laborne Kalil, MD, MS, and other experts about which treatments are proven to tackle hair loss at the root and promote regrowth.

🎙️This past week, @AnnaRRose & @nico_mnbl spoke w Alex Ozdemir, Assistant Professor at Georgia Tech, about the formal verification/ZK connection — covering SMT solvers, Lean, zkPi and why verifiable software matters.

✨ Glimpses from an engaging and thought-provoking session by Dr. Aakash Johry, Assistant Professor, Department of Design, IIT Delhi, on “A Critical Lens on Games: Serious Games and Playful Technology.” #IITDelhi# #DesignTalk# #GameDesign# #HCD# #InteractionDesign# #IIITDelhi#

Researchers at the University of Colorado Boulder have developed ORCA — the Open Radar Code Architecture — a free, open-source software framework that turns commercially available software-defined radios into functioning radar systems for environmental science, without requiring users to have a radar engineering background. The problem ORCA solves is a well-known frustration in radar research: every new student joining a lab typically spends up to a year rebuilding the same basic control code from scratch before any actual science can begin. "Every new student is just recreating the wheel over and over," said assistant professor Nicole Bienert. With ORCA, researchers buy a software-defined radio — now available for a few thousand dollars compared to hundreds of thousands for traditional radar systems — attach off-the-shelf antennas and cables, download the code, and have a functioning radar. Current applications include measuring glacier ice thickness in the Arctic, determining water content within trees, and studying ice sheet base conditions. The platform has grown through monthly meetings and online forums with participants from multiple universities across continents, and researchers can contribute their own code via GitHub. The initiative aims to make radar accessible to geophysicists, ecologists, and other scientists who have environmental questions but lack the technical background to build radar systems from scratch.

🦾 Why Data, Not Models, Is the Real Moat in Embodied AI The timing of this question is hard to miss. This week, the World Humanoid Robot Games released a 2,500+ hour dataset covering 12 scenario categories, 44 operations, and more than 10,000 tasks. Crucially, it also includes failures and edge cases. But raw hours tell only part of the story. Zhihu contributor 于超, an assistant professor at Tsinghua Shenzhen International Graduate School, shares his team’s view on why data has become embodied AI’s hardest-to-replicate advantage. 1️⃣ Robot scaling is fundamentally asymmetric Like language models, robot policies appear to benefit from more data, larger models, and greater compute. But these three inputs do not scale equally. Model architectures can be studied and reproduced quickly. General-purpose compute can, in principle, be purchased. High-quality robot data is different. It must be accumulated through physical interaction, and competitors cannot recreate it overnight. That asymmetry is what turns data into a moat. 2️⃣ Robot data must be manufactured LLMs inherited decades of internet data. Robots did not. Every useful trajectory must be produced through physical interaction. Even a simple cup-grasping task changes with the object, lighting, environment, camera angle, and robot body. So raw hours are not enough. What matters is the diversity of embodiments, tasks, objects, failures, and recoveries. Open X-Embodiment needed more than 20 institutions and 22 robot platforms to collect over one million trajectories. DROID used 50 collectors for a year, producing only 350 hours of data. New methods such as UMI and egocentric recording make collection easier. But every hour still requires real people, equipment, and time. 3️⃣ A successful trajectory can still be bad data Robot data quality is more complicated than whether a task was completed. On the hardware side, camera accuracy, encoder readings, force sensors, calibration, communication latency, and synchronization across modalities can all corrupt a trajectory. The human operator adds another source of noise. Teleoperating a robot is not the same as performing the action directly. Operators hesitate, pause, readjust, and develop habits for compensating for the control system. A task may succeed even when parts of the demonstration should never be imitated. Success is therefore only the coarsest possible label. One trajectory can contain both excellent behavior and inefficient or misleading actions. Training on the entire trajectory without distinction effectively tells the robot to learn both. 4️⃣ The next challenge is information density Collecting more trajectories is only half the problem. Teams must also identify which parts are worth learning from. Yu Chao’s team developed STEAM to detect local progress within a trajectory without frame-by-frame annotation or manually designed rewards. It separates useful progress from hesitation, failure, and recovery. The key question is shifting from “How many trajectories do we have?” to “How much useful information does each trajectory contain?” 5️⃣ Embodied data is physically expensive Text can be copied. Videos can be downloaded. Robot data requires a physical production process. Collecting one hour may involve a robot, sensors, teleoperation equipment, an operator, a suitable environment, task materials, and engineers who maintain and calibrate the system. Real factories, stores, and homes add even more complexity. And pressing the record button is only the beginning. Transmission, cleaning, governance, and storage can cost more than collection itself. The author offers a rough calculation. If a company wants one million hours of real-world data and reduces the combined collection and management cost to RMB 200 per hour, the total still reaches RMB 200 million. 🔑 The real moat compounds over time Quantity, quality, and cost explain why embodied AI data cannot be replicated through a short burst of spending. Large, diverse, high-quality datasets require physical infrastructure, operational discipline, and years of accumulation. As robot policies continue to benefit from scaling, the durable advantage will belong to teams that can repeatedly: 🔹 Collect broader real-world experience 🔹 Identify the most informative behavior 🔹 Preserve failures and recovery signals 🔹 Turn noisy trajectories into useful learning data In embodied AI, having data and knowing how to use it are becoming two very different capabilities. And the second may ultimately matter even more than the first. 🔗 Full analysis: #EmbodiedAI# #Robotics# #PhysicalAI# #RobotLearning# #AIData# #ScalingLaw# #Tsinghua#